
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__							:							int													=     logging.get_logger(__name__)

lowercase__							:							Union[str, Any]													=     {
    "mnaylor/mega-base-wikitext": "https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json",
}






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					'mega'



							def __init__(					self       ,		SCREAMING_SNAKE_CASE_=30522       ,		SCREAMING_SNAKE_CASE_=128       ,		SCREAMING_SNAKE_CASE_=4       ,		SCREAMING_SNAKE_CASE_=256       ,		SCREAMING_SNAKE_CASE_=16       ,		SCREAMING_SNAKE_CASE_=True       ,		SCREAMING_SNAKE_CASE_=64       ,		SCREAMING_SNAKE_CASE_=False       ,		SCREAMING_SNAKE_CASE_=-1       ,		SCREAMING_SNAKE_CASE_=None       ,		SCREAMING_SNAKE_CASE_=True       ,		SCREAMING_SNAKE_CASE_="scalenorm"       ,		SCREAMING_SNAKE_CASE_=True       ,		SCREAMING_SNAKE_CASE_="silu"       ,		SCREAMING_SNAKE_CASE_="softmax"       ,		SCREAMING_SNAKE_CASE_=0.1       ,		SCREAMING_SNAKE_CASE_=0.1       ,		SCREAMING_SNAKE_CASE_=0.1       ,		SCREAMING_SNAKE_CASE_=False       ,		SCREAMING_SNAKE_CASE_=True       ,		SCREAMING_SNAKE_CASE_=256       ,		SCREAMING_SNAKE_CASE_=True       ,		SCREAMING_SNAKE_CASE_=0.1       ,		SCREAMING_SNAKE_CASE_=2048       ,		SCREAMING_SNAKE_CASE_=False       ,		SCREAMING_SNAKE_CASE_=2       ,		SCREAMING_SNAKE_CASE_=0.0_2       ,		SCREAMING_SNAKE_CASE_=0.2       ,		SCREAMING_SNAKE_CASE_=0.0_2       ,		SCREAMING_SNAKE_CASE_=1.0       ,		SCREAMING_SNAKE_CASE_=1       ,		SCREAMING_SNAKE_CASE_=0       ,		SCREAMING_SNAKE_CASE_=2       ,		SCREAMING_SNAKE_CASE_="rotary"       ,		SCREAMING_SNAKE_CASE_=None       ,		SCREAMING_SNAKE_CASE_=True       ,		SCREAMING_SNAKE_CASE_=True       ,		**SCREAMING_SNAKE_CASE_       ,		)->				List[str]:





													'''simple docstring'''






													super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,		bos_token_id=SCREAMING_SNAKE_CASE_       ,		eos_token_id=SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     vocab_size
													__UpperCamelCase       					=     hidden_size
													__UpperCamelCase       					=     num_hidden_layers
													__UpperCamelCase       					=     activation
													__UpperCamelCase       					=     attention_activation
													__UpperCamelCase       					=     intermediate_size
													__UpperCamelCase       					=     ema_projection_size
													__UpperCamelCase       					=     bidirectional
													__UpperCamelCase       					=     shared_representation_size
													__UpperCamelCase       					=     use_chunking
													__UpperCamelCase       					=     chunk_size
													__UpperCamelCase       					=     truncation
													__UpperCamelCase       					=     normalize_before_mega
													__UpperCamelCase       					=     normalization_type
													__UpperCamelCase       					=     norm_affine
													__UpperCamelCase       					=     dropout_prob
													__UpperCamelCase       					=     hidden_dropout_prob
													__UpperCamelCase       					=     attention_probs_dropout_prob
													__UpperCamelCase       					=     use_feature_dropout
													__UpperCamelCase       					=     use_normalized_ffn
													__UpperCamelCase       					=     nffn_hidden_size
													__UpperCamelCase       					=     normalize_before_ffn
													__UpperCamelCase       					=     nffn_activation_dropout_prob
													__UpperCamelCase       					=     max_positions
													__UpperCamelCase       					=     add_token_type_embeddings
													__UpperCamelCase       					=     type_vocab_size
													__UpperCamelCase       					=     initializer_range
													__UpperCamelCase       					=     ema_delta_alpha_range
													__UpperCamelCase       					=     ema_beta_range
													__UpperCamelCase       					=     ema_gamma_omega_range
													__UpperCamelCase       					=     relative_positional_bias
													__UpperCamelCase       					=     use_cache
													__UpperCamelCase       					=     classifier_dropout
													__UpperCamelCase       					=     add_lm_hidden_dense_layer
													__UpperCamelCase       					=     1  # not used but required by Hugging Face






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






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





													'''simple docstring'''






													if self.task == "multiple-choice":
																			__UpperCamelCase       					=     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
													else:
																			__UpperCamelCase       					=     {0: '''batch''', 1: '''sequence'''}
													return OrderedDict(
													    [
													        ('''input_ids''', dynamic_axis),
													        ('''attention_mask''', dynamic_axis),
													    ]     )






import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available




def 		A_	(							snake_case							:				Union[str, Any]	, snake_case							:				str	, snake_case							:				Dict	, snake_case							:				Optional[Any]=None	) ->      Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     None
						# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
						if str(weight.dtype	) == "torch.bfloat16":
												# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
												__UpperCamelCase       					=     weight.view(torch.intaa	)
												__UpperCamelCase       					=     '''bfloat16'''
						__UpperCamelCase       					=     weight.cpu().numpy()
						__UpperCamelCase       					=     os.path.join(snake_case	, f"{weight_name}.dat"	)
						if index is not None:
												if dtype is None:
																		__UpperCamelCase       					=     str(array.dtype	)
												__UpperCamelCase       					=     {'''dtype''': dtype, '''shape''': list(array.shape	)}
						if array.ndim == 0:
												__UpperCamelCase       					=     array[None]
						__UpperCamelCase       					=     np.memmap(snake_case	, dtype=array.dtype	, mode='''w+'''	, shape=array.shape	)
						__UpperCamelCase       					=     array[:]
						file_array.flush()
						return index




def 		A_	(							snake_case							:				Tuple	, snake_case							:				Union[str, Any]	) ->      Dict:



						'''simple docstring'''

						__UpperCamelCase       					=     tuple(weight_info['''shape''']	)
						if shape == ():
												# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
												__UpperCamelCase       					=     (1,)

						__UpperCamelCase       					=     weight_info['''dtype''']
						if dtype == "bfloat16":
												# NumPy does not support bfloat16 so this was saved as a int16
												__UpperCamelCase       					=     '''int16'''

						__UpperCamelCase       					=     np.memmap(snake_case	, dtype=snake_case	, shape=snake_case	, mode='''r'''	)

						if len(weight_info['''shape''']	) == 0:
												__UpperCamelCase       					=     weight[0]
						__UpperCamelCase       					=     torch.tensor(snake_case	)
						if weight_info["dtype"] == "bfloat16":
												__UpperCamelCase       					=     weight.view(torch.bfloataa	)

						return weight




def 		A_	(							snake_case							:				Dict	, snake_case							:				int	) ->      str:



						'''simple docstring'''

						if index is None or len(snake_case	) == 0:
												# Nothing to save
												return

						__UpperCamelCase       					=     os.path.join(snake_case	, '''index.json'''	)
						if os.path.isfile(snake_case	):
												with open(snake_case	, '''r'''	, encoding='''utf-8'''	) as f:
																		__UpperCamelCase       					=     json.load(snake_case	)
						else:
												__UpperCamelCase       					=     {}
						current_index.update(snake_case	)

						with open(snake_case	, '''w'''	, encoding='''utf-8'''	) as f:
												json.dump(snake_case	, snake_case	, indent=2	)




def 		A_	(							snake_case							:				Union[str, os.PathLike]	, snake_case							:				Dict[str, torch.Tensor]	) ->      Dict:



						'''simple docstring'''

						os.makedirs(snake_case	, exist_ok=snake_case	)
						__UpperCamelCase       					=     {}
						for name, parameter in state_dict.items():
												__UpperCamelCase       					=     offload_weight(snake_case	, snake_case	, snake_case	, index=snake_case	)

						# Update index
						save_offload_index(snake_case	, snake_case	)






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     dataset
													__UpperCamelCase       					=     prefix



							def __getitem__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													return self.dataset[F"{self.prefix}{key}"]



							def __iter__(					self     )->				Union[str, Any]:





													'''simple docstring'''






													return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )



							def __len__(					self     )->				str:





													'''simple docstring'''






													return len(self.dataset     )






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_=None       ,		)->				Tuple:





													'''simple docstring'''






													if state_dict is None and save_folder is None:
																			raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''     )

													__UpperCamelCase       					=     {} if state_dict is None else state_dict
													__UpperCamelCase       					=     save_folder
													if index is None and save_folder is not None:
																			with open(os.path.join(SCREAMING_SNAKE_CASE_       ,		'''index.json'''     )     ) as f:
																									__UpperCamelCase       					=     json.load(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {} if index is None else index
													__UpperCamelCase       					=     list(self.state_dict.keys()     )
													self.all_keys.extend([key for key in self.index if key not in self.all_keys]     )
													__UpperCamelCase       					=     device



							def __getitem__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													if key in self.state_dict:
																			return self.state_dict[key]
													__UpperCamelCase       					=     self.index[key]
													if weight_info.get('''safetensors_file'''     ) is not None:
																			if not is_safetensors_available():
																									raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''     )

																			from safetensors import safe_open

																			__UpperCamelCase       					=     '''cpu''' if self.device is None else self.device
																			with safe_open(weight_info['''safetensors_file''']       ,		framework='''pt'''       ,		device=SCREAMING_SNAKE_CASE_     ) as f:
																									__UpperCamelCase       					=     f.get_tensor(weight_info.get('''weight_name'''       ,		SCREAMING_SNAKE_CASE_     )     )

																			if "dtype" in weight_info:
																									return tensor.to(getattr(SCREAMING_SNAKE_CASE_       ,		weight_info['''dtype''']     )     )
																			else:
																									return tensor

													__UpperCamelCase       					=     os.path.join(self.save_folder       ,		F"{key}.dat"     )
													return load_offloaded_weight(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def __iter__(					self     )->				Dict:





													'''simple docstring'''






													return iter(self.all_keys     )



							def __len__(					self     )->				Dict:





													'''simple docstring'''






													return len(self.all_keys     )





def 		A_	(							snake_case							:				Dict[str, torch.Tensor]	, snake_case							:				List[str]	) ->      Tuple:



						'''simple docstring'''

						__UpperCamelCase       					=     {}
						for module_name in submodule_names:
												# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
												# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
												result.update(
												    {
												        key: param
												        for key, param in state_dict.items()
												        if key == module_name or key.startswith(module_name + '''.'''	)
												    }	)
						return result




