






"""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__	:    List[str]			 = logging.get_logger(__name__)

A__	:    Dict			 = {
    '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        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Optional[Any]             =			"gpt_neo"
			_UpperCAmelCase		:Dict             =			["past_key_values"]
			_UpperCAmelCase		:Union[str, Any]             =			{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


			def __init__(				self					:  Tuple       ,	snake_case__					:  Optional[Any]=5_0257       ,	snake_case__					:  Optional[int]=2048       ,	snake_case__					:  Dict=2048       ,	snake_case__					:  int=24       ,	snake_case__					:  str=[[["global", "local"], 12]]       ,	snake_case__					:  Union[str, Any]=16       ,	snake_case__					:  Union[str, Any]=None       ,	snake_case__					:  Optional[int]=256       ,	snake_case__					:  int="gelu_new"       ,	snake_case__					:  Optional[int]=0.0       ,	snake_case__					:  List[Any]=0.0       ,	snake_case__					:  List[str]=0.0       ,	snake_case__					:  Union[str, Any]=0.1       ,	snake_case__					:  Any=1E-5       ,	snake_case__					:  List[Any]=0.02       ,	snake_case__					:  List[str]=True       ,	snake_case__					:  Dict=5_0256       ,	snake_case__					:  List[str]=5_0256       ,	**snake_case__					:  List[Any]       ,	):
										lowerCamelCase_	:							Tuple									=vocab_size
										lowerCamelCase_	:							Any									=max_position_embeddings
										lowerCamelCase_	:							int									=hidden_size
										lowerCamelCase_	:							int									=num_layers
										lowerCamelCase_	:							Union[str, Any]									=num_heads
										lowerCamelCase_	:							List[str]									=intermediate_size
										lowerCamelCase_	:							int									=window_size
										lowerCamelCase_	:							Any									=activation_function
										lowerCamelCase_	:							Dict									=resid_dropout
										lowerCamelCase_	:							Optional[Any]									=embed_dropout
										lowerCamelCase_	:							Tuple									=attention_dropout
										lowerCamelCase_	:							Tuple									=classifier_dropout
										lowerCamelCase_	:							List[Any]									=layer_norm_epsilon
										lowerCamelCase_	:							List[str]									=initializer_range
										lowerCamelCase_	:							Union[str, Any]									=use_cache

										lowerCamelCase_	:							Union[str, Any]									=bos_token_id
										lowerCamelCase_	:							Optional[int]									=eos_token_id

										lowerCamelCase_	:							Tuple									=attention_types
										lowerCamelCase_	:							Dict									=self.expand_attention_types_params(snake_case__ )

										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=snake_case__       ,	eos_token_id=snake_case__       ,	**snake_case__ )


			@staticmethod
			def      UpperCAmelCase__						(				snake_case__					:  str ):
										lowerCamelCase_	:							Any									=[]
										for item in attention_types:
																	for _ in range(item[1] ):
																								attentions.extend(item[0] )
										return attentions


def 			_snake_case    (					lowerCamelCase__     :   List[Any]    ,						lowerCamelCase__     :   Optional[Any]    ,						lowerCamelCase__     :   Tuple    ,						lowerCamelCase__     :   Any  )    ->     Optional[Any]:
							import torch

							lowerCamelCase_	:							Union[str, Any]									=input.size()
							lowerCamelCase_	:							Any									=len(lowerCamelCase__  )
							lowerCamelCase_	:							Optional[int]									=shape[dimension]

							lowerCamelCase_	:							Optional[Any]									=torch.arange(0    ,						lowerCamelCase__    ,						lowerCamelCase__  )
							lowerCamelCase_	:							List[str]									=torch.div(sizedim - size    ,						lowerCamelCase__    ,						rounding_mode="floor"  ) + 1
							lowerCamelCase_	:							List[Any]									=torch.arange(lowerCamelCase__  ) + low_indices[:min_length][:, None]

							lowerCamelCase_	:							Any									=[slice(lowerCamelCase__  )] * rank
							lowerCamelCase_	:							Tuple									=indices
							lowerCamelCase_	:							List[Any]									=input[s]

							lowerCamelCase_	:							Dict									=list(range(0    ,						rank + 1  )  )
							perm.append(perm.pop(dimension + 1  )  )

							return sliced.permute(lowerCamelCase__  )

def 			_snake_case    (					lowerCamelCase__     :   Union[str, Any]    ,						lowerCamelCase__     :   int  )    ->     Tuple:
							import torch

							lowerCamelCase_	:							str									=torch.arange(1    ,						lowerCamelCase__  )
							lowerCamelCase_	:							List[str]									=torch.remainder(lowerCamelCase__    ,						lowerCamelCase__  )
							lowerCamelCase_	:							Dict									=remainders == 0
							lowerCamelCase_	:							List[Any]									=candidates[divisor_indices]
							lowerCamelCase_	:							Optional[Any]									=torch.max(lowerCamelCase__  )
							return largest_divisor, torch.div(lowerCamelCase__    ,						lowerCamelCase__    ,						rounding_mode="floor"  )







class        lowercase__      (							snake_case__       ):


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							Tuple									=OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
										if self.use_past:
																	self.fill_with_past_key_values_(snake_case__       ,	direction="inputs" )
																	lowerCamelCase_	:							List[Any]									={0: "batch", 1: "past_sequence + sequence"}
										else:
																	lowerCamelCase_	:							Tuple									={0: "batch", 1: "sequence"}

										return common_inputs


			@property
			def      UpperCAmelCase__						(				self					:  List[Any] ):
										return self._config.num_heads


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  PreTrainedTokenizer       ,	snake_case__					:  int = -1       ,	snake_case__					:  int = -1       ,	snake_case__					:  bool = False       ,	snake_case__					:  Optional[TensorType] = None       ,	):
										lowerCamelCase_	:							Optional[int]									=super(snake_case__       ,	self ).generate_dummy_inputs(
										    snake_case__       ,	batch_size=snake_case__       ,	seq_length=snake_case__       ,	is_pair=snake_case__       ,	framework=snake_case__ )

										# We need to order the input in the way they appears in the forward()
										lowerCamelCase_	:							Optional[int]									=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_							,						lowerCamelCase_	:							List[Any]									=common_inputs["input_ids"].shape
																								# Not using the same length for past_key_values
																								lowerCamelCase_	:							List[str]									=seqlen + 2
																								lowerCamelCase_	:							List[str]									=(
																								    batch,
																								    self.num_attention_heads,
																								    past_key_values_length,
																								    self._config.hidden_size // self.num_attention_heads,
																								)
																								lowerCamelCase_	:							Optional[int]									=[
																								    (torch.zeros(snake_case__ ), torch.zeros(snake_case__ )) for _ in range(self.num_layers )
																								]

										lowerCamelCase_	:							Optional[Any]									=common_inputs["attention_mask"]
										if self.use_past:
																	lowerCamelCase_	:							Optional[int]									=ordered_inputs["attention_mask"].dtype
																	lowerCamelCase_	:							Tuple									=torch.cat(
																	    [ordered_inputs["attention_mask"], torch.ones(snake_case__       ,	snake_case__       ,	dtype=snake_case__ )]       ,	dim=1 )

										return ordered_inputs


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										return 13











"""simple docstring"""



from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


A__	:    Dict			 = logging.get_logger(__name__)

A__	:    Dict			 = {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

A__	:    List[Any]			 = {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

A__	:    Optional[int]			 = {
    'facebook/blenderbot_small-90M': 512,
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Optional[int]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Tuple             =			PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:Any             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Tuple             =			BlenderbotSmallTokenizer


			def __init__(				self					:  Tuple       ,	snake_case__					:  Optional[Any]=None       ,	snake_case__					:  str=None       ,	snake_case__					:  Any="<|endoftext|>"       ,	snake_case__					:  Tuple="<|endoftext|>"       ,	snake_case__					:  Tuple="<|endoftext|>"       ,	snake_case__					:  str=False       ,	snake_case__					:  int=True       ,	**snake_case__					:  Tuple       ,	):
										super().__init__(
										    ByteLevelBPETokenizer(
										        vocab=snake_case__       ,	merges=snake_case__       ,	add_prefix_space=snake_case__       ,	trim_offsets=snake_case__       ,	)       ,	bos_token=snake_case__       ,	eos_token=snake_case__       ,	unk_token=snake_case__       ,	**snake_case__       ,	)
										lowerCamelCase_	:							Optional[int]									=add_prefix_space


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[str]=None ):
										lowerCamelCase_	:							Optional[Any]									=[self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
																	return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  List[int]       ,	snake_case__					:  Optional[List[int]] = None ):
										lowerCamelCase_	:							int									=[self.sep_token_id]
										lowerCamelCase_	:							List[Any]									=[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 + sep + token_ids_a + sep ) * [0]



