"""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  UpperCamelCase	(    __lowerCamelCase						):
   lowercase							=						'''gpt_neo'''
   lowercase							=						['''past_key_values''']
   lowercase							=						{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
   def __init__(    self  ,__UpperCamelCase=5_0257  ,__UpperCamelCase=2048  ,__UpperCamelCase=2048  ,__UpperCamelCase=24  ,__UpperCamelCase=[[["global", "local"], 12]]  ,__UpperCamelCase=16  ,__UpperCamelCase=None  ,__UpperCamelCase=256  ,__UpperCamelCase="gelu_new"  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.1  ,__UpperCamelCase=1e-5  ,__UpperCamelCase=0.02  ,__UpperCamelCase=True  ,__UpperCamelCase=5_0256  ,__UpperCamelCase=5_0256  ,**__UpperCamelCase  ,)			->   Tuple:


        '''simple docstring'''


        lowercase_			:       int									=			vocab_size
        lowercase_			:       Optional[int]									=			max_position_embeddings
        lowercase_			:       List[str]									=			hidden_size
        lowercase_			:       List[Any]									=			num_layers
        lowercase_			:       Any									=			num_heads
        lowercase_			:       Tuple									=			intermediate_size
        lowercase_			:       Dict									=			window_size
        lowercase_			:       List[str]									=			activation_function
        lowercase_			:       Optional[Any]									=			resid_dropout
        lowercase_			:       Tuple									=			embed_dropout
        lowercase_			:       List[Any]									=			attention_dropout
        lowercase_			:       Union[str, Any]									=			classifier_dropout
        lowercase_			:       Tuple									=			layer_norm_epsilon
        lowercase_			:       Dict									=			initializer_range
        lowercase_			:       int									=			use_cache

        lowercase_			:       Dict									=			bos_token_id
        lowercase_			:       List[str]									=			eos_token_id

        lowercase_			:       Tuple									=			attention_types
        lowercase_			:       Union[str, Any]									=			self.expand_attention_types_params(a_    )

        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=a_  ,eos_token_id=a_  ,**a_    )







   @staticmethod
   def     _UpperCAmelCase     (    __UpperCamelCase    )			->   Dict:


        '''simple docstring'''


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






def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  Tuple  ,    __SCREAMING_SNAKE_CASE							:  str   ):
     import torch

     lowercase_			:       int									=			input.size()
     lowercase_			:       Tuple									=			len(lowerCAmelCase_   )
     lowercase_			:       str									=			shape[dimension]

     lowercase_			:       Tuple									=			torch.arange(0  ,    lowerCAmelCase_  ,    lowerCAmelCase_   )
     lowercase_			:       Optional[Any]									=			torch.div(sizedim - size  ,    lowerCAmelCase_  ,    rounding_mode='floor'   ) + 1
     lowercase_			:       int									=			torch.arange(lowerCAmelCase_   ) + low_indices[:min_length][:, None]

     lowercase_			:       Optional[int]									=			[slice(lowerCAmelCase_   )] * rank
     lowercase_			:       Any									=			indices
     lowercase_			:       Optional[int]									=			input[s]

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

     return sliced.permute(lowerCAmelCase_   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  Tuple   ):
     import torch

     lowercase_			:       int									=			torch.arange(1  ,    lowerCAmelCase_   )
     lowercase_			:       str									=			torch.remainder(lowerCAmelCase_  ,    lowerCAmelCase_   )
     lowercase_			:       Union[str, Any]									=			remainders == 0
     lowercase_			:       int									=			candidates[divisor_indices]
     lowercase_			:       Dict									=			torch.max(lowerCAmelCase_   )
     return largest_divisor, torch.div(lowerCAmelCase_  ,    lowerCAmelCase_  ,    rounding_mode='floor'   )







class  UpperCamelCase	(    __lowerCamelCase						):
   @property
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       Tuple									=			OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}    )
        if self.use_past:
             self.fill_with_past_key_values_(a_  ,direction='inputs'    )
             lowercase_			:       Optional[int]									=			{0: "batch", 1: "past_sequence + sequence"}
        else:
             lowercase_			:       Any									=			{0: "batch", 1: "sequence"}

        return common_inputs
   @property
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        return self._config.num_heads
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = -1  ,__UpperCamelCase = -1  ,__UpperCamelCase = False  ,__UpperCamelCase = None  ,)			->   List[str]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			super(a_  ,self    ).generate_dummy_inputs(
            a_  ,batch_size=a_  ,seq_length=a_  ,is_pair=a_  ,framework=a_    )

        # We need to order the input in the way they appears in the forward()
        lowercase_			:       str									=			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

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

        lowercase_			:       List[str]									=			common_inputs["attention_mask"]
        if self.use_past:
             lowercase_			:       Any									=			ordered_inputs["attention_mask"].dtype
             lowercase_			:       Optional[Any]									=			torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(a_  ,a_  ,dtype=a_    )]  ,dim=1    )

        return ordered_inputs







   @property
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        return 13


"""simple docstring"""







__SCREAMING_SNAKE_CASE								={
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}

