





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__A									=				logging.get_logger(__name__)

__A									=				"▁"

__A									=				{"vocab_file": "sentencepiece.bpe.model"}

__A									=				{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

__A									=				{
    "facebook/xglm-564M": 2048,
}



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			["input_ids", "attention_mask"]

 def __init__(self				:   List[Any]       , UpperCAmelCase_				:   List[Any]       , UpperCAmelCase_				:   Any="<s>"       , UpperCAmelCase_				:   Optional[int]="</s>"       , UpperCAmelCase_				:   List[Any]="</s>"       , UpperCAmelCase_				:   Union[str, Any]="<s>"       , UpperCAmelCase_				:   Any="<unk>"       , UpperCAmelCase_				:   str="<pad>"       , UpperCAmelCase_				:   Optional[Dict[str, Any]] = None       , **UpperCAmelCase_				:   str       , )       ->None:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  ={} if sp_model_kwargs is None else sp_model_kwargs

   # Compatibility with the original tokenizer
   lowerCamelCase__:      Tuple			  =7
   lowerCamelCase__:      int			  =[F"""<madeupword{i}>""" for i in range(self.num_madeup_words)]

   lowerCamelCase__:      str			  =kwargs.get("additional_special_tokens"       , [])
   kwargs["additional_special_tokens"] += [
       word for word in madeup_words if word not in kwargs["additional_special_tokens"]
   ]

   super().__init__(
       bos_token=UpperCAmelCase_       , eos_token=UpperCAmelCase_       , unk_token=UpperCAmelCase_       , sep_token=UpperCAmelCase_       , cls_token=UpperCAmelCase_       , pad_token=UpperCAmelCase_       , sp_model_kwargs=self.sp_model_kwargs       , **UpperCAmelCase_       , )

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

   # Original fairseq vocab and spm vocab must be "aligned":
   # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
   # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
   # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
   # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

   # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
   lowerCamelCase__:      Union[str, Any]			  =1

   # Mimic fairseq token-to-id alignment for the first 4 token
   lowerCamelCase__:      Union[str, Any]			  ={"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

   lowerCamelCase__:      Any			  =len(self.sp_model)
   lowerCamelCase__:      Tuple			  ={F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words)}
   self.fairseq_tokens_to_ids.update(UpperCAmelCase_)

   lowerCamelCase__:      int			  ={v: k for k, v in self.fairseq_tokens_to_ids.items()}

 def __getstate__(self				:   Dict)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =self.__dict__.copy()
   lowerCamelCase__:      str			  =None
   lowerCamelCase__:      Any			  =self.sp_model.serialized_model_proto()
   return state

 def __setstate__(self				:   Any       , UpperCAmelCase_				:   Tuple)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =d

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

   lowerCamelCase__:      str			  =spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.LoadFromSerializedProto(self.sp_model_proto)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   if token_ids_a is None:
     return [self.sep_token_id] + token_ids_a
   lowerCamelCase__:      Any			  =[self.sep_token_id]
   return sep + token_ids_a + sep + sep + token_ids_a

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None       , UpperCAmelCase_				:   bool = False)       ->List[int]:



   '''simple docstring'''



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

   if token_ids_a is None:
     return [1] + ([0] * len(UpperCAmelCase_))
   return [1] + ([0] * len(UpperCAmelCase_)) + [1, 1] + ([0] * len(UpperCAmelCase_))

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =[self.sep_token_id]

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

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Optional[int]:



   '''simple docstring'''



   return len(self.sp_model) + self.fairseq_offset + self.num_madeup_words

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      str			  ={self.convert_ids_to_tokens(UpperCAmelCase_): i for i in range(self.vocab_size)}
   vocab.update(self.added_tokens_encoder)
   return vocab

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   str)       ->List[str]:



   '''simple docstring'''



   return self.sp_model.encode(UpperCAmelCase_       , out_type=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   Union[str, Any])       ->Dict:



   '''simple docstring'''



   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   lowerCamelCase__:      Optional[int]			  =self.sp_model.PieceToId(UpperCAmelCase_)

   # Need to return unknown token if the SP model returned 0
   return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   List[str])       ->Optional[Any]:



   '''simple docstring'''



   if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(index - self.fairseq_offset)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   Dict)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      str			  ="".join(UpperCAmelCase_).replace(UpperCAmelCase_       , " ").strip()
   return out_string







 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[str] = None)       ->Tuple[str]:



   '''simple docstring'''



   if not os.path.isdir(UpperCAmelCase_):
     logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
     return
   lowerCamelCase__:      List[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:
       lowerCamelCase__:      Optional[int]			  =self.sp_model.serialized_model_proto()
       fi.write(UpperCAmelCase_)

   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


__lowerCamelCase					:	Optional[int]       =  logging.get_logger(__name__)

__lowerCamelCase					:	Tuple       =  {
    '''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 		a__						(					A__					):
					A	       =							'perceiver'

					def __init__(    self      :       List[Any],_A      :       Tuple=256,_A      :       str=1280,_A      :       List[Any]=768,_A      :       Union[str, Any]=1,_A      :       Union[str, Any]=26,_A      :       List[str]=8,_A      :       List[Any]=8,_A      :       List[Any]=None,_A      :       List[Any]=None,_A      :       Union[str, Any]="kv",_A      :       Any=1,_A      :       int=1,_A      :       Dict="gelu",_A      :       Any=0.1,_A      :       int=0.02,_A      :       int=1E-12,_A      :       Any=True,_A      :       Optional[Any]=262,_A      :       List[Any]=2048,_A      :       str=56,_A      :       Optional[int]=[368, 496],_A      :       Dict=16,_A      :       Tuple=1920,_A      :       List[Any]=16,_A      :       str=[1, 16, 224, 224],**_A      :       Optional[Any],):







							"""simple docstring"""






							super().__init__(**_A    )

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







class 		a__						(					A__					):

					@property
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							if self.task == "multiple-choice":
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    {0: "batch", 1: "choice", 2: "sequence"}
							else:
									SCREAMING_SNAKE_CASE_  :		str				      =    {0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("inputs", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]    )

					@property
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							return 1E-4






					def 				__UpperCamelCase   (    self      :       List[str],_A      :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],_A      :       int = -1,_A      :       int = -1,_A      :       int = -1,_A      :       bool = False,_A      :       Optional[TensorType] = None,_A      :       int = 3,_A      :       int = 40,_A      :       int = 40,):







							"""simple docstring"""






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