





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    (    _UpperCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""gpt_neo"""
			lowerCAmelCase_							     =						["""past_key_values"""]
			lowerCAmelCase_							     =						{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}





			def __init__(    self       ,  __lowerCAmelCase=5_0_2_5_7       ,  __lowerCAmelCase=2_0_4_8       ,  __lowerCAmelCase=2_0_4_8       ,  __lowerCAmelCase=2_4       ,  __lowerCAmelCase=[[["global", "local"], 1_2]]       ,  __lowerCAmelCase=1_6       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=2_5_6       ,  __lowerCAmelCase="gelu_new"       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=1E-5       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=5_0_2_5_6       ,  __lowerCAmelCase=5_0_2_5_6       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		vocab_size
				lowerCamelCase__								=		max_position_embeddings
				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		num_layers
				lowerCamelCase__								=		num_heads
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		window_size
				lowerCamelCase__								=		activation_function
				lowerCamelCase__								=		resid_dropout
				lowerCamelCase__								=		embed_dropout
				lowerCamelCase__								=		attention_dropout
				lowerCamelCase__								=		classifier_dropout
				lowerCamelCase__								=		layer_norm_epsilon
				lowerCamelCase__								=		initializer_range
				lowerCamelCase__								=		use_cache

				lowerCamelCase__								=		bos_token_id
				lowerCamelCase__								=		eos_token_id

				lowerCamelCase__								=		attention_types
				lowerCamelCase__								=		self.expand_attention_types_params(lowercase_							)

				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=lowercase_       ,  eos_token_id=lowercase_       ,  **lowercase_							)





			@staticmethod
			def 		__lowerCamelCase				(    __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		[]
				for item in attention_types:
					for _ in range(item[1]							):
						attentions.extend(item[0]							)
				return attentions





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case							)				->      Tuple:







	'''simple docstring'''







	import torch

	lowerCamelCase__								=		input.size()
	lowerCamelCase__								=		len(UpperCAmelCase__							)
	lowerCamelCase__								=		shape[dimension]

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

	lowerCamelCase__								=		[slice(UpperCAmelCase__							)] * rank
	lowerCamelCase__								=		indices
	lowerCamelCase__								=		input[s]

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

	return sliced.permute(UpperCAmelCase__							)





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      List[Any]:







	'''simple docstring'''







	import torch

	lowerCamelCase__								=		torch.arange(1       ,UpperCAmelCase__							)
	lowerCamelCase__								=		torch.remainder(UpperCAmelCase__       ,UpperCAmelCase__							)
	lowerCamelCase__								=		remainders == 0
	lowerCamelCase__								=		candidates[divisor_indices]
	lowerCamelCase__								=		torch.max(UpperCAmelCase__							)
	return largest_divisor, torch.div(UpperCAmelCase__       ,UpperCAmelCase__       ,rounding_mode='''floor'''							)






class 					__A    (    _UpperCAmelCase ):

			'''simple docstring'''







			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}							)
				if self.use_past:
					self.fill_with_past_key_values_(lowercase_       ,  direction='''inputs'''							)
					lowerCamelCase__								=		{0: """batch""", 1: """past_sequence + sequence"""}
				else:
					lowerCamelCase__								=		{0: """batch""", 1: """sequence"""}

				return common_inputs





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self._config.num_heads





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = -1       ,  __lowerCAmelCase = -1       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = None       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		super(lowercase_       ,  self							).generate_dummy_inputs(
				    lowercase_       ,  batch_size=lowercase_       ,  seq_length=lowercase_       ,  is_pair=lowercase_       ,  framework=lowercase_							)

				# We need to order the input in the way they appears in the forward()
				lowerCamelCase__								=		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

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

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

				return ordered_inputs





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				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_albert import AlbertTokenizer
else:
						_lowercase						: int						=						None

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

_lowercase						: Dict						=						{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

_lowercase						: int						=						{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

_lowercase						: Tuple						=						"▁"





class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       VOCAB_FILES_NAMES
			UpperCamelCase__				=       PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__				=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__				=       AlbertTokenizer







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  Union[str, Any]=None				,			lowercase_				:  str=None				,			lowercase_				:  Any=True				,			lowercase_				:  Optional[int]=True				,			lowercase_				:  List[str]=False				,			lowercase_				:  Optional[int]="[CLS]"				,			lowercase_				:  Any="[SEP]"				,			lowercase_				:  int="<unk>"				,			lowercase_				:  Any="[SEP]"				,			lowercase_				:  int="<pad>"				,			lowercase_				:  Tuple="[CLS]"				,			lowercase_				:  Dict="[MASK]"				,			**lowercase_				:  Optional[int]				,			):
										# 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.
										lowercase_					:			Tuple										=       (
										    AddedToken(lowercase_				,			lstrip=lowercase_				,			rstrip=lowercase_				,			normalized=lowercase_    )
										    if isinstance(lowercase_				,			lowercase_    )
										    else mask_token
										)

										super().__init__(
										    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_				,			**lowercase_				,			)

										lowercase_					:			Optional[int]										=       do_lower_case
										lowercase_					:			Any										=       remove_space
										lowercase_					:			Dict										=       keep_accents
										lowercase_					:			Optional[int]										=       vocab_file
										lowercase_					:			Any										=       False if not self.vocab_file else True







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  List[int]				,			lowercase_				:  Optional[List[int]] = None    ):
										lowercase_					:			Tuple										=       [self.sep_token_id]
										lowercase_					:			str										=       [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 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  List[int]				,			lowercase_				:  Optional[List[int]] = None    ):
										lowercase_					:			Union[str, Any]										=       [self.sep_token_id]
										lowercase_					:			int										=       [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 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]				,			lowercase_				:  str				,			lowercase_				:  Optional[str] = None    ):
										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
										lowercase_					:			Optional[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_    ):
																	copyfile(self.vocab_file				,			lowercase_    )

										return (out_vocab_file,)





