



"""simple docstring"""


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

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


__A										=   logging.get_logger(__name__)

__A										=   {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class 			_a (					__lowercase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""gpt_neo"""
			UpperCamelCase__			      =	["""past_key_values"""]
			UpperCamelCase__			      =	{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



			def __init__(		self     :    str				,   __UpperCamelCase     :    Any=5_0_2_5_7				,   __UpperCamelCase     :    Union[str, Any]=2_0_4_8				,   __UpperCamelCase     :    Optional[Any]=2_0_4_8				,   __UpperCamelCase     :    Dict=2_4				,   __UpperCamelCase     :    Dict=[[["global", "local"], 1_2]]				,   __UpperCamelCase     :    List[str]=1_6				,   __UpperCamelCase     :    Optional[Any]=None				,   __UpperCamelCase     :    Optional[Any]=2_5_6				,   __UpperCamelCase     :    List[Any]="gelu_new"				,   __UpperCamelCase     :    str=0.0				,   __UpperCamelCase     :    Optional[Any]=0.0				,   __UpperCamelCase     :    Any=0.0				,   __UpperCamelCase     :    List[str]=0.1				,   __UpperCamelCase     :    Optional[Any]=1e-5				,   __UpperCamelCase     :    List[str]=0.0_2				,   __UpperCamelCase     :    Optional[int]=True				,   __UpperCamelCase     :    Dict=5_0_2_5_6				,   __UpperCamelCase     :    str=5_0_2_5_6				,   **__UpperCamelCase     :    List[Any]				,   )->Optional[Any]:
							_UpperCAmelCase					=    vocab_size
							_UpperCAmelCase					=    max_position_embeddings
							_UpperCAmelCase					=    hidden_size
							_UpperCAmelCase					=    num_layers
							_UpperCAmelCase					=    num_heads
							_UpperCAmelCase					=    intermediate_size
							_UpperCAmelCase					=    window_size
							_UpperCAmelCase					=    activation_function
							_UpperCAmelCase					=    resid_dropout
							_UpperCAmelCase					=    embed_dropout
							_UpperCAmelCase					=    attention_dropout
							_UpperCAmelCase					=    classifier_dropout
							_UpperCAmelCase					=    layer_norm_epsilon
							_UpperCAmelCase					=    initializer_range
							_UpperCAmelCase					=    use_cache

							_UpperCAmelCase					=    bos_token_id
							_UpperCAmelCase					=    eos_token_id

							_UpperCAmelCase					=    attention_types
							_UpperCAmelCase					=    self.expand_attention_types_params(__UpperCamelCase   )

							if len(self.attention_layers   ) != self.num_layers:
											raise ValueError(
											    '''Configuration for convolutional module is incorrect. '''
											    '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
											    F'but is `len(config.attention_layers) = {len(self.attention_layers   )}`, '
											    F'`config.num_layers = {self.num_layers}`. '
											    '''`config.attention_layers` is prepared using `config.attention_types`. '''
											    '''Please verify the value of `config.attention_types` argument.'''   )

							super().__init__(bos_token_id=__UpperCamelCase				,   eos_token_id=__UpperCamelCase				,   **__UpperCamelCase   )



			@staticmethod
			def  lowercase__						(		__UpperCamelCase     :    List[Any]   )->Any:
							_UpperCAmelCase					=    []
							for item in attention_types:
											for _ in range(item[1]   ):
															attentions.extend(item[0]   )
							return attentions







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				import torch

				_UpperCAmelCase					=    input.size()
				_UpperCAmelCase					=    len(_lowerCamelCase				)
				_UpperCAmelCase					=    shape[dimension]

				_UpperCAmelCase					=    torch.arange(0					,     _lowerCamelCase					,     _lowerCamelCase				)
				_UpperCAmelCase					=    torch.div(sizedim - size					,     _lowerCamelCase					,     rounding_mode='''floor'''				) + 1
				_UpperCAmelCase					=    torch.arange(_lowerCamelCase				) + low_indices[:min_length][:, None]

				_UpperCAmelCase					=    [slice(_lowerCamelCase				)] * rank
				_UpperCAmelCase					=    indices
				_UpperCAmelCase					=    input[s]

				_UpperCAmelCase					=    list(range(0					,     rank + 1				)				)
				perm.append(perm.pop(dimension + 1				)				)

				return sliced.permute(_lowerCamelCase				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Tuple					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				import torch

				_UpperCAmelCase					=    torch.arange(1					,     _lowerCamelCase				)
				_UpperCAmelCase					=    torch.remainder(_lowerCamelCase					,     _lowerCamelCase				)
				_UpperCAmelCase					=    remainders == 0
				_UpperCAmelCase					=    candidates[divisor_indices]
				_UpperCAmelCase					=    torch.max(_lowerCamelCase				)
				return largest_divisor, torch.div(_lowerCamelCase					,     _lowerCamelCase					,     rounding_mode='''floor'''				)
class 			_a (					__lowercase):
			"""simple docstring"""



			@property
			def  lowercase__						(		self     :    int   )->Mapping[str, Mapping[int, str]]:
							_UpperCAmelCase					=    OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}   )
							if self.use_past:
											self.fill_with_past_key_values_(__UpperCamelCase				,   direction='''inputs'''   )
											_UpperCAmelCase					=    {0: "batch", 1: "past_sequence + sequence"}
							else:
											_UpperCAmelCase					=    {0: "batch", 1: "sequence"}

							return common_inputs



			@property
			def  lowercase__						(		self     :    Any   )->int:
							return self._config.num_heads



			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    PreTrainedTokenizer				,   __UpperCamelCase     :    int = -1				,   __UpperCamelCase     :    int = -1				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    Optional[TensorType] = None				,   )->Mapping[str, Any]:
							_UpperCAmelCase					=    super(__UpperCamelCase				,   self   ).generate_dummy_inputs(
							    __UpperCamelCase				,   batch_size=__UpperCamelCase				,   seq_length=__UpperCamelCase				,   is_pair=__UpperCamelCase				,   framework=__UpperCamelCase   )

							# We need to order the input in the way they appears in the forward()
							_UpperCAmelCase					=    OrderedDict({'''input_ids''': common_inputs['''input_ids''']}   )

							# Need to add the past_keys
							if self.use_past:
											if not is_torch_available():
															raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
											else:
															import torch

															_UpperCAmelCase					=    common_inputs["input_ids"].shape
															# Not using the same length for past_key_values
															_UpperCAmelCase					=    seqlen + 2
															_UpperCAmelCase					=    (
															    batch,
															    self.num_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // self.num_attention_heads,
															)
															_UpperCAmelCase					=    [
															    (torch.zeros(__UpperCamelCase   ), torch.zeros(__UpperCamelCase   )) for _ in range(self.num_layers   )
															]

							_UpperCAmelCase					=    common_inputs["attention_mask"]
							if self.use_past:
											_UpperCAmelCase					=    ordered_inputs["attention_mask"].dtype
											_UpperCAmelCase					=    torch.cat(
											    [ordered_inputs['''attention_mask'''], torch.ones(__UpperCamelCase				,   __UpperCamelCase				,   dtype=__UpperCamelCase   )]				,   dim=1   )

							return ordered_inputs



			@property
			def  lowercase__						(		self     :    Union[str, Any]   )->int:
							return 1_3








"""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_fnet import FNetTokenizer
else:
					__A   :							Tuple										=   None

__A   :							Optional[Any]										=   logging.get_logger(__name__)
__A   :							Union[str, Any]										=   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A   :							int										=   {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

__A   :							Optional[int]										=   {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

__A   :							List[Any]										=   "▁"
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	VOCAB_FILES_NAMES
			UpperCamelCase__			      =	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__			      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__			      =	["""input_ids""", """token_type_ids"""]
			UpperCamelCase__			      =	FNetTokenizer



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Tuple=None				,   __UpperCamelCase     :    Dict=None				,   __UpperCamelCase     :    List[str]=False				,   __UpperCamelCase     :    Union[str, Any]=True				,   __UpperCamelCase     :    List[str]=True				,   __UpperCamelCase     :    List[Any]="<unk>"				,   __UpperCamelCase     :    Any="[SEP]"				,   __UpperCamelCase     :    Dict="<pad>"				,   __UpperCamelCase     :    Optional[int]="[CLS]"				,   __UpperCamelCase     :    List[Any]="[MASK]"				,   **__UpperCamelCase     :    Optional[Any]				,   )->Dict:
							# Mask token behave like a normal word, i.e. include the space before it and
							# is included in the raw text, there should be a match in a non-normalized sentence.
							_UpperCAmelCase					=    (
							    AddedToken(__UpperCamelCase				,   lstrip=__UpperCamelCase				,   rstrip=__UpperCamelCase				,   normalized=__UpperCamelCase   )
							    if isinstance(__UpperCamelCase				,   __UpperCamelCase   )
							    else mask_token
							)

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

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



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [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  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [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  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Optional[str] = None   )->Tuple[str]:
							if not os.path.isdir(__UpperCamelCase   ):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
											return
							_UpperCAmelCase					=    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   ):
											copyfile(self.vocab_file				,   __UpperCamelCase   )

							return (out_vocab_file,)



