





'''simple docstring'''




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

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


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							     =			{
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "blenderbot-small"
     snake_case_						      =   ["past_key_values"]
     snake_case_						      =   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



     def __init__(   self							:			List[str]   ,       __snake_case							:			str=5_02_65   ,       __snake_case							:			Optional[int]=5_12   ,       __snake_case							:			Any=8   ,       __snake_case							:			str=20_48   ,       __snake_case							:			str=16   ,       __snake_case							:			Tuple=8   ,       __snake_case							:			Any=20_48   ,       __snake_case							:			Optional[Any]=16   ,       __snake_case							:			Optional[int]=0.0   ,       __snake_case							:			Any=0.0   ,       __snake_case							:			Dict=True   ,       __snake_case							:			List[Any]=True   ,       __snake_case							:			int="gelu"   ,       __snake_case							:			List[Any]=5_12   ,       __snake_case							:			Optional[int]=0.1   ,       __snake_case							:			List[Any]=0.0   ,       __snake_case							:			Union[str, Any]=0.0   ,       __snake_case							:			List[str]=0.02   ,       __snake_case							:			Optional[Any]=1   ,       __snake_case							:			Dict=False   ,       __snake_case							:			Dict=0   ,       __snake_case							:			str=1   ,       __snake_case							:			Optional[Any]=2   ,       __snake_case							:			Tuple=2   ,       **__snake_case							:			Any   ,       )->	str:
        snake_case 						=   vocab_size
        snake_case 						=   max_position_embeddings
        snake_case 						=   d_model
        snake_case 						=   encoder_ffn_dim
        snake_case 						=   encoder_layers
        snake_case 						=   encoder_attention_heads
        snake_case 						=   decoder_ffn_dim
        snake_case 						=   decoder_layers
        snake_case 						=   decoder_attention_heads
        snake_case 						=   dropout
        snake_case 						=   attention_dropout
        snake_case 						=   activation_dropout
        snake_case 						=   activation_function
        snake_case 						=   init_std
        snake_case 						=   encoder_layerdrop
        snake_case 						=   decoder_layerdrop
        snake_case 						=   use_cache
        snake_case 						=   encoder_layers
        snake_case 						=   scale_embedding  # scale factor will be sqrt(d_model) if True

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




class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






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

           if self.use_past:
              snake_case 						=   {0: """batch"""}
              snake_case 						=   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
              snake_case 						=   {0: """batch""", 1: """decoder_sequence"""}
              snake_case 						=   {0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
              self.fill_with_past_key_values_(__snake_case   ,       direction="""inputs"""						)
        elif self.task == "causal-lm":
           # TODO: figure this case out.
           snake_case 						=   OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]						)
           if self.use_past:
              snake_case						,    snake_case 						=   self.num_layers
              for i in range(__snake_case						):
                 snake_case 						=   {0: """batch""", 2: """past_sequence + sequence"""}
                 snake_case 						=   {0: """batch""", 2: """past_sequence + sequence"""}
        else:
           snake_case 						=   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      lowerCAmelCase			(   self							:			Optional[int]						)->	Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
           snake_case 						=   super().outputs
        else:
           snake_case 						=   super(__snake_case   ,       self						).outputs
           if self.use_past:
              snake_case						,    snake_case 						=   self.num_layers
              for i in range(__snake_case						):
                 snake_case 						=   {0: """batch""", 2: """past_sequence + sequence"""}
                 snake_case 						=   {0: """batch""", 2: """past_sequence + sequence"""}
        return common_outputs



     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			PreTrainedTokenizer   ,       __snake_case							:			int = -1   ,       __snake_case							:			int = -1   ,       __snake_case							:			bool = False   ,       __snake_case							:			Optional[TensorType] = None   ,       )->	Mapping[str, Any]:
        snake_case 						=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case						)

        # Generate decoder inputs
        snake_case 						=   seq_length if not self.use_past else 1
        snake_case 						=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case						)
        snake_case 						=   {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
        snake_case 						=   dict(**__snake_case   ,       **__snake_case						)

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

           snake_case 						=   torch.cat(
               [common_inputs["""decoder_attention_mask"""], torch.ones(__snake_case   ,       __snake_case						)]   ,       dim=1						)

           snake_case 						=   []
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           snake_case						,    snake_case 						=   self.num_layers
           snake_case 						=   min(__snake_case   ,       __snake_case						)
           snake_case 						=   max(__snake_case   ,       __snake_case						) - min_num_layers
           snake_case 						=   """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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



     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			PreTrainedTokenizer   ,       __snake_case							:			int = -1   ,       __snake_case							:			int = -1   ,       __snake_case							:			bool = False   ,       __snake_case							:			Optional[TensorType] = None   ,       )->	Mapping[str, Any]:
        snake_case 						=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case						)

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

           snake_case 						=   common_inputs["""attention_mask"""].dtype
           snake_case 						=   torch.cat(
               [common_inputs["""attention_mask"""], torch.ones(__snake_case   ,       __snake_case   ,       dtype=__snake_case						)]   ,       dim=1						)
           snake_case 						=   [
               (torch.zeros(__snake_case						), torch.zeros(__snake_case						)) for _ in range(__snake_case						)
           ]
        return common_inputs



     def      lowerCAmelCase			(   self							:			Union[str, Any]   ,       __snake_case							:			PreTrainedTokenizer   ,       __snake_case							:			int = -1   ,       __snake_case							:			int = -1   ,       __snake_case							:			bool = False   ,       __snake_case							:			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
        snake_case 						=   compute_effective_axis_dimension(
            __snake_case   ,       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
        snake_case 						=   tokenizer.num_special_tokens_to_add(__snake_case						)
        snake_case 						=   compute_effective_axis_dimension(
            __snake_case   ,       fixed_dimension=OnnxConfig.default_fixed_sequence   ,       num_token_to_add=__snake_case						)

        # Generate dummy inputs according to compute batch and sequence
        snake_case 						=   [""" """.join([tokenizer.unk_token]						) * seq_length] * batch_size
        snake_case 						=   dict(tokenizer(__snake_case   ,       return_tensors=__snake_case						)						)
        return common_inputs



     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			PreTrainedTokenizer   ,       __snake_case							:			int = -1   ,       __snake_case							:			int = -1   ,       __snake_case							:			bool = False   ,       __snake_case							:			Optional[TensorType] = None   ,       )->	Mapping[str, Any]:
        if self.task in ["default", "seq2seq-lm"]:
           snake_case 						=   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               __snake_case   ,       batch_size=__snake_case   ,       seq_length=__snake_case   ,       is_pair=__snake_case   ,       framework=__snake_case						)

        elif self.task == "causal-lm":
           snake_case 						=   self._generate_dummy_inputs_for_causal_lm(
               __snake_case   ,       batch_size=__snake_case   ,       seq_length=__snake_case   ,       is_pair=__snake_case   ,       framework=__snake_case						)
        else:
           snake_case 						=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               __snake_case   ,       batch_size=__snake_case   ,       seq_length=__snake_case   ,       is_pair=__snake_case   ,       framework=__snake_case						)

        return common_inputs



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			int   ,       __snake_case							:			List[str]   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			Dict						)->	Any:
        if self.task in ["default", "seq2seq-lm"]:
           snake_case 						=   super()._flatten_past_key_values_(__snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case						)
        else:
           snake_case 						=   super(__snake_case   ,       self						)._flatten_past_key_values_(
               __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case						)








'''simple docstring'''




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

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


if is_sentencepiece_available():
 from .tokenization_rembert import RemBertTokenizer
else:
 _SCREAMING_SNAKE_CASE							     =			None

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

_SCREAMING_SNAKE_CASE							     =			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

_SCREAMING_SNAKE_CASE							     =			{
    "google/rembert": 256,
}

_SCREAMING_SNAKE_CASE							     =			"▁"
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   VOCAB_FILES_NAMES
     snake_case_						      =   PRETRAINED_VOCAB_FILES_MAP
     snake_case_						      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case_						      =   RemBertTokenizer



     def __init__(   self							:			List[Any]   ,       __snake_case							:			int=None   ,       __snake_case							:			Union[str, Any]=None   ,       __snake_case							:			Tuple=True   ,       __snake_case							:			Dict=True   ,       __snake_case							:			str=False   ,       __snake_case							:			Union[str, Any]="[CLS]"   ,       __snake_case							:			Optional[int]="[SEP]"   ,       __snake_case							:			str="<unk>"   ,       __snake_case							:			Dict="[SEP]"   ,       __snake_case							:			Dict="<pad>"   ,       __snake_case							:			Union[str, Any]="[CLS]"   ,       __snake_case							:			int="[MASK]"   ,       **__snake_case							:			Optional[int]   ,       )->	Tuple:
        # Mask token behave like a normal word, i.e. include the space before it
        snake_case 						=   AddedToken(__snake_case   ,       lstrip=__snake_case   ,       rstrip=__snake_case						) if isinstance(__snake_case   ,       __snake_case						) else mask_token

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

        snake_case 						=   do_lower_case
        snake_case 						=   remove_space
        snake_case 						=   keep_accents
        snake_case 						=   vocab_file
        snake_case 						=   False if not self.vocab_file else True



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None						)->	List[int]:
        snake_case 						=   [self.sep_token_id]
        snake_case 						=   [self.cls_token_id]
        if token_ids_a is None:
           return cls + token_ids_a + sep
        return cls + token_ids_a + sep + token_ids_a + sep



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None   ,       __snake_case							:			bool = False						)->	List[int]:

        if already_has_special_tokens:
           if token_ids_a is not None:
              raise ValueError(
                  """You should not supply a second sequence if the provided sequence of """
                  """ids is already formatted with special tokens for the model."""						)
           return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

        if token_ids_a is not None:
           return [1] + ([0] * len(__snake_case						)) + [1] + ([0] * len(__snake_case						)) + [1]
        return [1] + ([0] * len(__snake_case						)) + [1]



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None						)->	List[int]:
        snake_case 						=   [self.sep_token_id]
        snake_case 						=   [self.cls_token_id]

        if token_ids_a is None:
           return len(cls + token_ids_a + sep						) * [0]
        return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]



     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			str   ,       __snake_case							:			Optional[str] = None						)->	Tuple[str]:
        if not os.path.isdir(__snake_case						):
           logger.error("""Vocabulary path ({}) should be a directory""".format(__snake_case						)						)
           return
        snake_case 						=   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						):
           copyfile(self.vocab_file   ,       __snake_case						)

        return (out_vocab_file,)

