





'''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


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							     =			{
    "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 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






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



     def __init__(   self							:			Tuple   ,       __snake_case							:			List[str]=5_02_57   ,       __snake_case							:			int=20_48   ,       __snake_case							:			List[str]=20_48   ,       __snake_case							:			str=24   ,       __snake_case							:			Tuple=[[["global", "local"], 12]]   ,       __snake_case							:			Optional[int]=16   ,       __snake_case							:			Any=None   ,       __snake_case							:			List[Any]=2_56   ,       __snake_case							:			Union[str, Any]="gelu_new"   ,       __snake_case							:			Optional[int]=0.0   ,       __snake_case							:			Tuple=0.0   ,       __snake_case							:			Optional[int]=0.0   ,       __snake_case							:			List[Any]=0.1   ,       __snake_case							:			Tuple=1e-5   ,       __snake_case							:			str=0.02   ,       __snake_case							:			Dict=True   ,       __snake_case							:			Optional[int]=5_02_56   ,       __snake_case							:			List[Any]=5_02_56   ,       **__snake_case							:			Tuple   ,       )->	Optional[int]:
        snake_case 						=   vocab_size
        snake_case 						=   max_position_embeddings
        snake_case 						=   hidden_size
        snake_case 						=   num_layers
        snake_case 						=   num_heads
        snake_case 						=   intermediate_size
        snake_case 						=   window_size
        snake_case 						=   activation_function
        snake_case 						=   resid_dropout
        snake_case 						=   embed_dropout
        snake_case 						=   attention_dropout
        snake_case 						=   classifier_dropout
        snake_case 						=   layer_norm_epsilon
        snake_case 						=   initializer_range
        snake_case 						=   use_cache

        snake_case 						=   bos_token_id
        snake_case 						=   eos_token_id

        snake_case 						=   attention_types
        snake_case 						=   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      lowerCAmelCase			(   __snake_case							:			List[str]						)->	Optional[Any]:
        snake_case 						=   []
        for item in attention_types:
           for _ in range(item[1]						):
              attentions.extend(item[0]						)
        return attentions





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Dict		)		->  str:
   import torch

   snake_case 						=   input.size()
   snake_case 						=   len(__lowerCAmelCase		)
   snake_case 						=   shape[dimension]

   snake_case 						=   torch.arange(0      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   snake_case 						=   torch.div(sizedim - size      ,    __lowerCAmelCase      ,    rounding_mode="""floor"""		) + 1
   snake_case 						=   torch.arange(__lowerCAmelCase		) + low_indices[:min_length][:, None]

   snake_case 						=   [slice(__lowerCAmelCase		)] * rank
   snake_case 						=   indices
   snake_case 						=   input[s]

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

   return sliced.permute(__lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[Any]      ,    __lowerCAmelCase       :							Union[str, Any]		)		->  Optional[Any]:
   import torch

   snake_case 						=   torch.arange(1      ,    __lowerCAmelCase		)
   snake_case 						=   torch.remainder(__lowerCAmelCase      ,    __lowerCAmelCase		)
   snake_case 						=   remainders == 0
   snake_case 						=   candidates[divisor_indices]
   snake_case 						=   torch.max(__lowerCAmelCase		)
   return largest_divisor, torch.div(__lowerCAmelCase      ,    __lowerCAmelCase      ,    rounding_mode="""floor"""		)


class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     @property
     def      lowerCAmelCase			(   self							:			List[Any]						)->	Mapping[str, Mapping[int, str]]:
        snake_case 						=   OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}						)
        if self.use_past:
           self.fill_with_past_key_values_(__snake_case   ,       direction="""inputs"""						)
           snake_case 						=   {0: """batch""", 1: """past_sequence + sequence"""}
        else:
           snake_case 						=   {0: """batch""", 1: """sequence"""}

        return common_inputs



     @property
     def      lowerCAmelCase			(   self							:			Optional[int]						)->	int:
        return self._config.num_heads



     def      lowerCAmelCase			(   self							:			Dict   ,       __snake_case							:			PreTrainedTokenizer   ,       __snake_case							:			int = -1   ,       __snake_case							:			int = -1   ,       __snake_case							:			bool = False   ,       __snake_case							:			Optional[TensorType] = None   ,       )->	Mapping[str, Any]:
        snake_case 						=   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()
        snake_case 						=   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

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

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

        return ordered_inputs



     @property
     def      lowerCAmelCase			(   self							:			Tuple						)->	int:
        return 13








'''simple docstring'''




from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)

