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

SCREAMING_SNAKE_CASE__		:       str       						=       {
    "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    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  'gpt_neo'
  lowercase_								=  ['past_key_values']
  lowercase_								=  {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}




  def __init__(   self  :						List[Any]  ,			a_  :						Union[str, Any]=5_0257  ,			a_  :						Any=2048  ,			a_  :						Dict=2048  ,			a_  :						Optional[Any]=24  ,			a_  :						List[str]=[[["global", "local"], 12]]  ,			a_  :						List[Any]=16  ,			a_  :						List[Any]=None  ,			a_  :						Tuple=256  ,			a_  :						List[Any]="gelu_new"  ,			a_  :						Optional[Any]=0.0  ,			a_  :						Optional[Any]=0.0  ,			a_  :						Dict=0.0  ,			a_  :						Any=0.1  ,			a_  :						List[Any]=1e-5  ,			a_  :						List[str]=0.02  ,			a_  :						List[str]=True  ,			a_  :						Tuple=5_0256  ,			a_  :						str=5_0256  ,			**a_  :						str  ,			)->							Optional[int]:




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

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		bos_token_id
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		eos_token_id

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		attention_types
       SCREAMING_SNAKE_CASE__					:					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 						__lowercase(   a_  :						Tuple					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		[]
       for item in attention_types:
            for _ in range(item[1]					):
                 attentions.extend(item[0]					)
       return attentions







def      _a					(   lowercase__	:      List[str]  ,	lowercase__	:      str  ,	lowercase__	:      Optional[int]  ,	lowercase__	:      str			):







     '''simple docstring'''


     import torch

     SCREAMING_SNAKE_CASE__					:					Any      =		input.size()
     SCREAMING_SNAKE_CASE__					:					Dict      =		len(lowercase__			)
     SCREAMING_SNAKE_CASE__					:					List[str]      =		shape[dimension]

     SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.arange(0  ,	lowercase__  ,	lowercase__			)
     SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.div(sizedim - size  ,	lowercase__  ,	rounding_mode='floor'			) + 1
     SCREAMING_SNAKE_CASE__					:					int      =		torch.arange(lowercase__			) + low_indices[:min_length][:, None]

     SCREAMING_SNAKE_CASE__					:					Any      =		[slice(lowercase__			)] * rank
     SCREAMING_SNAKE_CASE__					:					List[str]      =		indices
     SCREAMING_SNAKE_CASE__					:					Tuple      =		input[s]

     SCREAMING_SNAKE_CASE__					:					List[str]      =		list(range(0  ,	rank + 1			)			)
     perm.append(perm.pop(dimension + 1			)			)

     return sliced.permute(lowercase__			)







def      _a					(   lowercase__	:      str  ,	lowercase__	:      str			):







     '''simple docstring'''


     import torch

     SCREAMING_SNAKE_CASE__					:					Any      =		torch.arange(1  ,	lowercase__			)
     SCREAMING_SNAKE_CASE__					:					Any      =		torch.remainder(lowercase__  ,	lowercase__			)
     SCREAMING_SNAKE_CASE__					:					Any      =		remainders == 0
     SCREAMING_SNAKE_CASE__					:					str      =		candidates[divisor_indices]
     SCREAMING_SNAKE_CASE__					:					Any      =		torch.max(lowercase__			)
     return largest_divisor, torch.div(lowercase__  ,	lowercase__  ,	rounding_mode='floor'			)







class    snake_case       (   UpperCamelCase_    ):




  @property
  def 						__lowercase(   self  :						Optional[int]					)->							Mapping[str, Mapping[int, str]]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}					)
       if self.use_past:
            self.fill_with_past_key_values_(a_  ,			direction='inputs'					)
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		{0: 'batch', 1: 'past_sequence + sequence'}
       else:
            SCREAMING_SNAKE_CASE__					:					str      =		{0: 'batch', 1: 'sequence'}

       return common_inputs




  @property
  def 						__lowercase(   self  :						str					)->							int:




       """simple docstring"""
       return self._config.num_heads




  def 						__lowercase(   self  :						Optional[int]  ,			a_  :						PreTrainedTokenizer  ,			a_  :						int = -1  ,			a_  :						int = -1  ,			a_  :						bool = False  ,			a_  :						Optional[TensorType] = None  ,			)->							Mapping[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		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()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		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

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

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

       return ordered_inputs




  @property
  def 						__lowercase(   self  :						Dict					)->							int:




       """simple docstring"""
       return 13











from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	''''''
				a__       							=	'''hf-legacy'''  # "hf://"" is reserved for hffs







				def __init__(    self							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								super().__init__(self							,       **__lowerCAmelCase  )
								__magic_name__		:List[Any]   =   repo_info
								__magic_name__		:Dict   =   token
								__magic_name__		:Optional[Any]   =   None







				def       A						(    self  ):
								"""simple docstring"""
								if self.dir_cache is None:
												__magic_name__		:Any   =   {}
												for hf_file in self.repo_info.siblings:
																# TODO(QL): add sizes
																__magic_name__		:Optional[int]   =   {
																    '''name''': hf_file.rfilename,
																    '''size''': None,
																    '''type''': '''file''',
																}
																self.dir_cache.update(
																    {
																        str(__lowerCAmelCase  ): {'''name''': str(__lowerCAmelCase  ), '''size''': None, '''type''': '''directory'''}
																        for d in list(PurePosixPath(hf_file.rfilename  ).parents  )[:-1]
																    }  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = "rb"							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								if not isinstance(self.repo_info							,       __lowerCAmelCase  ):
												raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''  )
								__magic_name__		:Union[str, Any]   =   hf_hub_url(self.repo_info.id							,       __lowerCAmelCase							,       revision=self.repo_info.sha  )
								return fsspec.open(
								    __lowerCAmelCase							,       mode=__lowerCAmelCase							,       headers=get_authentication_headers_for_url(__lowerCAmelCase							,       use_auth_token=self.token  )							,       client_kwargs={'''trust_env''': True}							,       ).open()







				def       A						(    self							,       __lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								self._get_dirs()
								__magic_name__		:str   =   self._strip_protocol(__lowerCAmelCase  )
								if path in self.dir_cache:
												return self.dir_cache[path]
								else:
												raise FileNotFoundError(__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=False							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								self._get_dirs()
								__magic_name__		:Union[str, Any]   =   PurePosixPath(path.strip('''/'''  )  )
								__magic_name__		:Dict   =   {}
								for p, f in self.dir_cache.items():
												__magic_name__		:int   =   PurePosixPath(p.strip('''/'''  )  )
												__magic_name__		:Tuple   =   p.parent
												if root == path:
																__magic_name__		:Optional[Any]   =   f
								__magic_name__		:List[Any]   =   list(paths.values()  )
								if detail:
												return out
								else:
												return sorted(f['''name'''] for f in out  )






