






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

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


a		:	List[Any]      					=			logging.get_logger(__name__)

a		:	Union[str, Any]      					=			{
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}
class    a   (			lowercase__       ):



						"""simple docstring"""


						a  :			int								=     'mbart'
						a  :			Optional[int]								=     ['past_key_values']
						a  :			int								=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
						def __init__(				self :      Dict		,				__lowercase :      Optional[Any]=50265		,				__lowercase :      str=1024		,				__lowercase :      Tuple=12		,				__lowercase :      int=4096		,				__lowercase :      Any=16		,				__lowercase :      str=12		,				__lowercase :      Dict=4096		,				__lowercase :      Optional[int]=16		,				__lowercase :      List[Any]=0.0		,				__lowercase :      Dict=0.0		,				__lowercase :      Dict=True		,				__lowercase :      Tuple=True		,				__lowercase :      Optional[Any]="gelu"		,				__lowercase :      List[str]=1024		,				__lowercase :      Optional[Any]=0.1		,				__lowercase :      Union[str, Any]=0.0		,				__lowercase :      Any=0.0		,				__lowercase :      Optional[int]=0.02		,				__lowercase :      int=0.0		,				__lowercase :      Union[str, Any]=False		,				__lowercase :      Dict=1		,				__lowercase :      Union[str, Any]=0		,				__lowercase :      str=2		,				__lowercase :      Optional[Any]=2		,				**__lowercase :      List[str]		,				)							->				List[str]:
								__UpperCAmelCase							:     Union[str, Any]									=  vocab_size
								__UpperCAmelCase							:     Tuple									=  max_position_embeddings
								__UpperCAmelCase							:     Dict									=  d_model
								__UpperCAmelCase							:     List[str]									=  encoder_ffn_dim
								__UpperCAmelCase							:     Optional[Any]									=  encoder_layers
								__UpperCAmelCase							:     Optional[int]									=  encoder_attention_heads
								__UpperCAmelCase							:     Tuple									=  decoder_ffn_dim
								__UpperCAmelCase							:     Optional[Any]									=  decoder_layers
								__UpperCAmelCase							:     List[str]									=  decoder_attention_heads
								__UpperCAmelCase							:     List[str]									=  dropout
								__UpperCAmelCase							:     Union[str, Any]									=  attention_dropout
								__UpperCAmelCase							:     int									=  activation_dropout
								__UpperCAmelCase							:     int									=  activation_function
								__UpperCAmelCase							:     int									=  init_std
								__UpperCAmelCase							:     Tuple									=  encoder_layerdrop
								__UpperCAmelCase							:     List[Any]									=  decoder_layerdrop
								__UpperCAmelCase							:     Dict									=  classifier_dropout
								__UpperCAmelCase							:     Tuple									=  use_cache
								__UpperCAmelCase							:     Any									=  encoder_layers
								__UpperCAmelCase							:     int									=  scale_embedding  # scale factor will be sqrt(d_model) if True
								super().__init__(
								    pad_token_id=__lowercase		,				bos_token_id=__lowercase		,				eos_token_id=__lowercase		,				is_encoder_decoder=__lowercase		,				forced_eos_token_id=__lowercase		,				**__lowercase		,				)


class    a   (			lowercase__       ):



						"""simple docstring"""


						@property
						def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
										__UpperCAmelCase							:     Optional[int]									=  OrderedDict(
										    [
										        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
										        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
										    ] )

										if self.use_past:
												__UpperCAmelCase							:     List[Any]									=  {0: """batch"""}
												__UpperCAmelCase							:     Optional[int]									=  {0: """batch""", 1: """past_decoder_sequence + sequence"""}
										else:
												__UpperCAmelCase							:     Optional[Any]									=  {0: """batch""", 1: """decoder_sequence"""}
												__UpperCAmelCase							:     List[Any]									=  {0: """batch""", 1: """decoder_sequence"""}

										if self.use_past:
												self.fill_with_past_key_values_(__lowercase		,				direction="""inputs""" )
								elif self.task == "causal-lm":
										# TODO: figure this case out.
										__UpperCAmelCase							:     Optional[Any]									=  OrderedDict(
										    [
										        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
										        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
										    ] )
										if self.use_past:
												__UpperCAmelCase							:     Optional[int]									=  self.num_layers
												for i in range(__lowercase ):
														__UpperCAmelCase							:     List[str]									=  {0: """batch""", 2: """past_sequence + sequence"""}
														__UpperCAmelCase							:     Optional[int]									=  {0: """batch""", 2: """past_sequence + sequence"""}
								else:
										__UpperCAmelCase							:     List[Any]									=  OrderedDict(
										    [
										        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
										        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
										        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
										        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
										    ] )

								return common_inputs
						@property
						def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
										__UpperCAmelCase							:     Optional[Any]									=  super().outputs
								else:
										__UpperCAmelCase							:     Any									=  super(__lowercase		,				self ).outputs
										if self.use_past:
												__UpperCAmelCase							:     List[Any]									=  self.num_layers
												for i in range(__lowercase ):
														__UpperCAmelCase							:     Tuple									=  {0: """batch""", 2: """past_sequence + sequence"""}
														__UpperCAmelCase							:     Tuple									=  {0: """batch""", 2: """past_sequence + sequence"""}
								return common_outputs
						def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      PreTrainedTokenizer		,				__lowercase :      int = -1		,				__lowercase :      int = -1		,				__lowercase :      bool = False		,				__lowercase :      Optional[TensorType] = None		,				)							->				Mapping[str, Any]:
								__UpperCAmelCase							:     Any									=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				__lowercase )

								# Generate decoder inputs
								__UpperCAmelCase							:     str									=  seq_length if not self.use_past else 1
								__UpperCAmelCase							:     int									=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				__lowercase )
								__UpperCAmelCase							:     List[Any]									=  {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
								__UpperCAmelCase							:     List[Any]									=  dict(**__lowercase		,				**__lowercase )

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

										__UpperCAmelCase							:     List[Any]									=  torch.cat(
										    [common_inputs["""decoder_attention_mask"""], torch.ones(__lowercase		,				__lowercase )]		,				dim=1 )

										__UpperCAmelCase							:     str									=  []
										# If the number of encoder and decoder layers are present in the model configuration, both are considered
										__UpperCAmelCase							:     Any									=  self.num_layers
										__UpperCAmelCase							:     int									=  min(__lowercase		,				__lowercase )
										__UpperCAmelCase							:     Optional[Any]									=  max(__lowercase		,				__lowercase ) - min_num_layers
										__UpperCAmelCase							:     List[str]									=  """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

										for _ in range(__lowercase ):
												common_inputs["past_key_values"].append(
												    (
												        torch.zeros(__lowercase ),
												        torch.zeros(__lowercase ),
												        torch.zeros(__lowercase ),
												        torch.zeros(__lowercase ),
												    ) )
										# TODO: test this.
										__UpperCAmelCase							:     str									=  encoder_shape if remaining_side_name == """encoder""" else decoder_shape
										for _ in range(__lowercase		,				__lowercase ):
												common_inputs["past_key_values"].append((torch.zeros(__lowercase ), torch.zeros(__lowercase )) )
								return common_inputs
						def      UpperCAmelCase				(				self :      Optional[int]		,				__lowercase :      PreTrainedTokenizer		,				__lowercase :      int = -1		,				__lowercase :      int = -1		,				__lowercase :      bool = False		,				__lowercase :      Optional[TensorType] = None		,				)							->				Mapping[str, Any]:
								__UpperCAmelCase							:     Any									=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				__lowercase )

								if self.use_past:
										if not is_torch_available():
												raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
										else:
												import torch
										__UpperCAmelCase							:     List[str]									=  common_inputs["""input_ids"""].shape
										# Not using the same length for past_key_values
										__UpperCAmelCase							:     int									=  seqlen + 2
										__UpperCAmelCase							:     Optional[int]									=  self.num_layers
										__UpperCAmelCase							:     Any									=  self.num_attention_heads
										__UpperCAmelCase							:     Tuple									=  (
										    batch,
										    num_encoder_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)

										__UpperCAmelCase							:     str									=  common_inputs["""attention_mask"""].dtype
										__UpperCAmelCase							:     Optional[int]									=  torch.cat(
										    [common_inputs["""attention_mask"""], torch.ones(__lowercase		,				__lowercase		,				dtype=__lowercase )]		,				dim=1 )
										__UpperCAmelCase							:     List[Any]									=  [
										    (torch.zeros(__lowercase ), torch.zeros(__lowercase )) for _ in range(__lowercase )
										]
								return common_inputs
						def      UpperCAmelCase				(				self :      int		,				__lowercase :      PreTrainedTokenizer		,				__lowercase :      int = -1		,				__lowercase :      int = -1		,				__lowercase :      bool = False		,				__lowercase :      Optional[TensorType] = None		,				)							->				Mapping[str, Any]:
								# 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[Any]									=  compute_effective_axis_dimension(
								    __lowercase		,				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							:     Tuple									=  tokenizer.num_special_tokens_to_add(__lowercase )
								__UpperCAmelCase							:     List[Any]									=  compute_effective_axis_dimension(
								    __lowercase		,				fixed_dimension=OnnxConfig.default_fixed_sequence		,				num_token_to_add=__lowercase )

								# Generate dummy inputs according to compute batch and sequence
								__UpperCAmelCase							:     int									=  [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
								__UpperCAmelCase							:     Tuple									=  dict(tokenizer(__lowercase		,				return_tensors=__lowercase ) )
								return common_inputs
						def      UpperCAmelCase				(				self :      Any		,				__lowercase :      PreTrainedTokenizer		,				__lowercase :      int = -1		,				__lowercase :      int = -1		,				__lowercase :      bool = False		,				__lowercase :      Optional[TensorType] = None		,				)							->				Mapping[str, Any]:
								if self.task in ["default", "seq2seq-lm"]:
										__UpperCAmelCase							:     int									=  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
										    __lowercase		,				batch_size=__lowercase		,				seq_length=__lowercase		,				is_pair=__lowercase		,				framework=__lowercase )

								elif self.task == "causal-lm":
										__UpperCAmelCase							:     Optional[Any]									=  self._generate_dummy_inputs_for_causal_lm(
										    __lowercase		,				batch_size=__lowercase		,				seq_length=__lowercase		,				is_pair=__lowercase		,				framework=__lowercase )
								else:
										__UpperCAmelCase							:     Union[str, Any]									=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    __lowercase		,				batch_size=__lowercase		,				seq_length=__lowercase		,				is_pair=__lowercase		,				framework=__lowercase )

								return common_inputs




						def      UpperCAmelCase				(				self :      Optional[int]		,				__lowercase :      List[Any]		,				__lowercase :      int		,				__lowercase :      List[str]		,				__lowercase :      int )							->				Optional[Any]:
								if self.task in ["default", "seq2seq-lm"]:
										__UpperCAmelCase							:     Optional[Any]									=  super()._flatten_past_key_values_(__lowercase		,				__lowercase		,				__lowercase		,				__lowercase )
								else:
										__UpperCAmelCase							:     Tuple									=  super(__lowercase		,				self )._flatten_past_key_values_(
										    __lowercase		,				__lowercase		,				__lowercase		,				__lowercase )










import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


a		:	Any      					=			logging.get_logger(__name__)

a		:	Dict      					=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a		:	Optional[int]      					=			{
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json",
    },
}
class    a   (			lowercase__       ):



      """simple docstring"""


      def __init__(				self :      List[Any]		,				__lowercase :      Union[str, Any]=None		,				__lowercase :      Tuple=None		,				__lowercase :      Optional[int]=False		,				__lowercase :      Any=True		,				__lowercase :      List[str]=False		,				__lowercase :      Union[str, Any]="<s>"		,				__lowercase :      List[str]="</s>"		,				__lowercase :      Dict="<unk>"		,				__lowercase :      List[str]="<sep>"		,				__lowercase :      int="<pad>"		,				__lowercase :      Any="<cls>"		,				__lowercase :      int="<mask>"		,				__lowercase :      Tuple=["<eop>", "<eod>"]		,				**__lowercase :      Union[str, Any]		,				)							->				Optional[Any]:
        __UpperCAmelCase							:     Any									=  AddedToken(__lowercase		,				lstrip=__lowercase		,				rstrip=__lowercase ) if isinstance(__lowercase		,				__lowercase ) else mask_token

        super().__init__(
            vocab_file=__lowercase		,				tokenizer_file=__lowercase		,				do_lower_case=__lowercase		,				remove_space=__lowercase		,				keep_accents=__lowercase		,				bos_token=__lowercase		,				eos_token=__lowercase		,				unk_token=__lowercase		,				sep_token=__lowercase		,				pad_token=__lowercase		,				cls_token=__lowercase		,				mask_token=__lowercase		,				additional_special_tokens=__lowercase		,				**__lowercase		,				)

        __UpperCAmelCase							:     Optional[int]									=  3
        __UpperCAmelCase							:     Dict									=  do_lower_case
        __UpperCAmelCase							:     Union[str, Any]									=  remove_space
        __UpperCAmelCase							:     Tuple									=  keep_accents
        __UpperCAmelCase							:     int									=  vocab_file
        __UpperCAmelCase							:     Dict									=  False if not self.vocab_file else True

        try:
          import jieba
        except ModuleNotFoundError as error:
          raise error.__class__(
              """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
              """See https://pypi.org/project/jieba/ for installation.""" )
        __UpperCAmelCase							:     Dict									=  jieba
        __UpperCAmelCase							:     str									=  str.maketrans(""" \n"""		,				"""\u2582\u2583""" )
      def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      List[int]		,				__lowercase :      Optional[List[int]] = None )							->				List[int]:
        __UpperCAmelCase							:     List[str]									=  [self.sep_token_id]
        __UpperCAmelCase							:     List[str]									=  [self.cls_token_id]
        if token_ids_a is None:
          return token_ids_a + sep + cls
        return token_ids_a + sep + token_ids_a + sep + cls
      def      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      List[int]		,				__lowercase :      Optional[List[int]] = None )							->				List[int]:
        __UpperCAmelCase							:     Dict									=  [self.sep_token_id]
        __UpperCAmelCase							:     List[str]									=  [2]

        if token_ids_a is None:
          return len(token_ids_a + sep ) * [0] + cls_segment_id
        return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
      def      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      str		,				__lowercase :      Optional[str] = None )							->				Tuple[str]:
        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(__lowercase ):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
          return
        __UpperCAmelCase							:     List[str]									=  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 ):
          copyfile(self.vocab_file		,				__lowercase )

        return (out_vocab_file,)
      def      UpperCAmelCase				(				self :      Optional[Any]		,				__lowercase :      Any		,				*__lowercase :      Dict		,				**__lowercase :      int )							->				Optional[Any]:
        __UpperCAmelCase							:     List[str]									=  [
            """ """.join([x.translate(self.translator ) for x in self.jieba.cut(__lowercase		,				cut_all=__lowercase )] )
            for text in batch_text_or_text_pairs
        ]
        return super()._batch_encode_plus(__lowercase		,				*__lowercase		,				**__lowercase )




      def      UpperCAmelCase				(				self :      int		,				*__lowercase :      List[Any]		,				**__lowercase :      Tuple )							->				List[str]:
        __UpperCAmelCase							:     List[str]									=  super()._decode(*__lowercase		,				**__lowercase )
        __UpperCAmelCase							:     List[Any]									=  text.replace(""" """		,				"""""" ).replace("""\u2582"""		,				""" """ ).replace("""\u2583"""		,				"""\n""" )
        return text


