





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

import sentencepiece as spm

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


UpperCAmelCase_					:   Tuple 							=						logging.get_logger(__name__)

UpperCAmelCase_					:   str 							=						{"""vocab_file""": """spiece.model"""}

UpperCAmelCase_					:   List[str] 							=						{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

UpperCAmelCase_					:   int 							=						{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
UpperCAmelCase_					:   List[Any] 							=						0
UpperCAmelCase_					:   Union[str, Any] 							=						1
UpperCAmelCase_					:   Optional[int] 							=						2
UpperCAmelCase_					:   Dict 							=						3
UpperCAmelCase_					:   Optional[int] 							=						4





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  VOCAB_FILES_NAMES
  __UpperCamelCase	     =  PRETRAINED_VOCAB_FILES_MAP
  __UpperCamelCase	     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __UpperCamelCase	     =  """left"""







  def __init__(	self    ,						_lowercase    ,						_lowercase=False    ,						_lowercase=True    ,						_lowercase=False    ,						_lowercase="<s>"    ,						_lowercase="</s>"    ,						_lowercase="<unk>"    ,						_lowercase="<sep>"    ,						_lowercase="<pad>"    ,						_lowercase="<cls>"    ,						_lowercase="<mask>"    ,						_lowercase=["<eop>", "<eod>"]    ,						_lowercase = None    ,						**_lowercase    ,						):
       # Mask token behave like a normal word, i.e. include the space before it
       lowerCAmelCase_					:       str    =		AddedToken(_lowercase    ,						lstrip=_lowercase    ,						rstrip=_lowercase						) if isinstance(_lowercase    ,						_lowercase						) else mask_token

       lowerCAmelCase_					:       Optional[Any]    =		{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           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    ,						sp_model_kwargs=self.sp_model_kwargs    ,						**_lowercase    ,						)

       lowerCAmelCase_					:       Optional[int]    =		3

       lowerCAmelCase_					:       Tuple    =		do_lower_case
       lowerCAmelCase_					:       Optional[Any]    =		remove_space
       lowerCAmelCase_					:       List[str]    =		keep_accents
       lowerCAmelCase_					:       Optional[int]    =		vocab_file

       lowerCAmelCase_					:       Union[str, Any]    =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(_lowercase						)







  @property
  def 	UpperCAmelCase__		(	self						):
       return len(self.sp_model						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       str    =		{self.convert_ids_to_tokens(_lowercase						): i for i in range(self.vocab_size						)}
       vocab.update(self.added_tokens_encoder						)
       return vocab







  def __getstate__(	self						):
       lowerCAmelCase_					:       int    =		self.__dict__.copy()
       lowerCAmelCase_					:       Any    =		None
       return state







  def __setstate__(	self    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		d

       # for backward compatibility
       if not hasattr(self    ,						"""sp_model_kwargs"""						):
            lowerCAmelCase_					:       int    =		{}

       lowerCAmelCase_					:       List[Any]    =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(self.vocab_file						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       if self.remove_space:
            lowerCAmelCase_					:       int    =		""" """.join(inputs.strip().split()						)
       else:
            lowerCAmelCase_					:       Any    =		inputs
       lowerCAmelCase_					:       Union[str, Any]    =		outputs.replace("""``"""    ,						"""\""""						).replace("""''"""    ,						"""\""""						)

       if not self.keep_accents:
            lowerCAmelCase_					:       Optional[int]    =		unicodedata.normalize("""NFKD"""    ,						_lowercase						)
            lowerCAmelCase_					:       List[Any]    =		"""""".join([c for c in outputs if not unicodedata.combining(_lowercase						)]						)
       if self.do_lower_case:
            lowerCAmelCase_					:       List[str]    =		outputs.lower()

       return outputs







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       lowerCAmelCase_					:       Tuple    =		self.preprocess_text(_lowercase						)
       lowerCAmelCase_					:       Tuple    =		self.sp_model.encode(_lowercase    ,						out_type=_lowercase						)
       lowerCAmelCase_					:       str    =		[]
       for piece in pieces:
            if len(_lowercase						) > 1 and piece[-1] == str(""","""						) and piece[-2].isdigit():
                 lowerCAmelCase_					:       Optional[Any]    =		self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowercase    ,						""""""						)						)
                 if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                      if len(cur_pieces[0]						) == 1:
                           lowerCAmelCase_					:       Optional[Any]    =		cur_pieces[1:]
                      else:
                           lowerCAmelCase_					:       int    =		cur_pieces[0][1:]
                 cur_pieces.append(piece[-1]						)
                 new_pieces.extend(_lowercase						)
            else:
                 new_pieces.append(_lowercase						)

       return new_pieces







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       return self.sp_model.PieceToId(_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       return self.sp_model.IdToPiece(_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       lowerCAmelCase_					:       Any    =		"""""".join(_lowercase						).replace(_lowercase    ,						""" """						).strip()
       return out_string







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = False    ,						_lowercase = None    ,						_lowercase = True    ,						**_lowercase    ,						):
       lowerCAmelCase_					:       int    =		kwargs.pop("""use_source_tokenizer"""    ,						_lowercase						)

       lowerCAmelCase_					:       Union[str, Any]    =		self.convert_ids_to_tokens(_lowercase    ,						skip_special_tokens=_lowercase						)

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       lowerCAmelCase_					:       List[Any]    =		[]
       lowerCAmelCase_					:       Optional[int]    =		[]
       for token in filtered_tokens:
            if skip_special_tokens and token in self.all_special_ids:
                 continue
            if token in self.added_tokens_encoder:
                 if current_sub_text:
                      sub_texts.append(self.convert_tokens_to_string(_lowercase						)						)
                      lowerCAmelCase_					:       Optional[int]    =		[]
                 sub_texts.append(_lowercase						)
            else:
                 current_sub_text.append(_lowercase						)
       if current_sub_text:
            sub_texts.append(self.convert_tokens_to_string(_lowercase						)						)

       # Mimic the behavior of the Rust tokenizer:
       # By default, there are no spaces between special tokens
       lowerCAmelCase_					:       Union[str, Any]    =		"""""".join(_lowercase						)

       lowerCAmelCase_					:       Any    =		(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
            lowerCAmelCase_					:       List[Any]    =		self.clean_up_tokenization(_lowercase						)
            return clean_text
       else:
            return text







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       Tuple    =		[self.sep_token_id]
       lowerCAmelCase_					:       Tuple    =		[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    ,						_lowercase    ,						_lowercase = None    ,						_lowercase = False						):

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

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







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       Any    =		[self.sep_token_id]
       lowerCAmelCase_					:       int    =		[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    ,						_lowercase    ,						_lowercase = None						):
       if not os.path.isdir(_lowercase						):
            logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
            return
       lowerCAmelCase_					:       Any    =		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						) and os.path.isfile(self.vocab_file						):
            copyfile(self.vocab_file    ,						_lowercase						)
       elif not os.path.isfile(self.vocab_file						):
            with open(_lowercase    ,						"""wb"""						) as fi:
                 lowerCAmelCase_					:       int    =		self.sp_model.serialized_model_proto()
                 fi.write(_lowercase						)

       return (out_vocab_file,)







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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


UpperCAmelCase_					:   Optional[Any] 							=						logging.get_logger(__name__)

UpperCAmelCase_					:   Any 							=						{
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """perceiver"""







  def __init__(	self    ,						_lowercase=256    ,						_lowercase=1_280    ,						_lowercase=768    ,						_lowercase=1    ,						_lowercase=26    ,						_lowercase=8    ,						_lowercase=8    ,						_lowercase=None    ,						_lowercase=None    ,						_lowercase="kv"    ,						_lowercase=1    ,						_lowercase=1    ,						_lowercase="gelu"    ,						_lowercase=0.1    ,						_lowercase=0.02    ,						_lowercase=1e-12    ,						_lowercase=True    ,						_lowercase=262    ,						_lowercase=2_048    ,						_lowercase=56    ,						_lowercase=[368, 496]    ,						_lowercase=16    ,						_lowercase=1_920    ,						_lowercase=16    ,						_lowercase=[1, 16, 224, 224]    ,						**_lowercase    ,						):
       super().__init__(**_lowercase						)

       lowerCAmelCase_					:       Optional[int]    =		num_latents
       lowerCAmelCase_					:       List[str]    =		d_latents
       lowerCAmelCase_					:       int    =		d_model
       lowerCAmelCase_					:       Dict    =		num_blocks
       lowerCAmelCase_					:       Union[str, Any]    =		num_self_attends_per_block
       lowerCAmelCase_					:       List[str]    =		num_self_attention_heads
       lowerCAmelCase_					:       List[str]    =		num_cross_attention_heads
       lowerCAmelCase_					:       List[Any]    =		qk_channels
       lowerCAmelCase_					:       Optional[Any]    =		v_channels
       lowerCAmelCase_					:       Optional[Any]    =		cross_attention_shape_for_attention
       lowerCAmelCase_					:       Optional[int]    =		self_attention_widening_factor
       lowerCAmelCase_					:       int    =		cross_attention_widening_factor
       lowerCAmelCase_					:       Union[str, Any]    =		hidden_act
       lowerCAmelCase_					:       int    =		attention_probs_dropout_prob
       lowerCAmelCase_					:       int    =		initializer_range
       lowerCAmelCase_					:       Optional[Any]    =		layer_norm_eps
       lowerCAmelCase_					:       Optional[Any]    =		use_query_residual
       # masked language modeling attributes
       lowerCAmelCase_					:       Tuple    =		vocab_size
       lowerCAmelCase_					:       List[Any]    =		max_position_embeddings
       # image classification attributes
       lowerCAmelCase_					:       List[Any]    =		image_size
       # flow attributes
       lowerCAmelCase_					:       Dict    =		train_size
       # multimodal autoencoding attributes
       lowerCAmelCase_					:       Optional[Any]    =		num_frames
       lowerCAmelCase_					:       int    =		audio_samples_per_frame
       lowerCAmelCase_					:       Any    =		samples_per_patch
       lowerCAmelCase_					:       Any    =		output_shape





class      lowercase__    (							__A  ):







  @property
  def 	UpperCAmelCase__		(	self						):
       if self.task == "multiple-choice":
            lowerCAmelCase_					:       Optional[Any]    =		{0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
            lowerCAmelCase_					:       Optional[int]    =		{0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""inputs""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]						)







  @property
  def 	UpperCAmelCase__		(	self						):
       return 1e-4







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = -1    ,						_lowercase = -1    ,						_lowercase = -1    ,						_lowercase = False    ,						_lowercase = None    ,						_lowercase = 3    ,						_lowercase = 40    ,						_lowercase = 40    ,						):
       # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

       if isinstance(_lowercase    ,						_lowercase						):
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            lowerCAmelCase_					:       Union[str, 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
            lowerCAmelCase_					:       int    =		preprocessor.num_special_tokens_to_add(_lowercase						)
            lowerCAmelCase_					:       int    =		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
            lowerCAmelCase_					:       Optional[int]    =		[""" """.join(["""a"""]						) * seq_length] * batch_size
            lowerCAmelCase_					:       Optional[Any]    =		dict(preprocessor(_lowercase    ,						return_tensors=_lowercase						)						)
            lowerCAmelCase_					:       Optional[Any]    =		inputs.pop("""input_ids"""						)
            return inputs
       elif isinstance(_lowercase    ,						_lowercase						) and preprocessor.model_input_names[0] == "pixel_values":
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            lowerCAmelCase_					:       Any    =		compute_effective_axis_dimension(_lowercase    ,						fixed_dimension=OnnxConfig.default_fixed_batch						)
            lowerCAmelCase_					:       str    =		self._generate_dummy_images(_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase						)
            lowerCAmelCase_					:       List[str]    =		dict(preprocessor(images=_lowercase    ,						return_tensors=_lowercase						)						)
            lowerCAmelCase_					:       Tuple    =		inputs.pop("""pixel_values"""						)
            return inputs
       else:
            raise ValueError(
                """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."""						)
