




from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase :       Any     					=    logging.get_logger(__name__)

_lowerCamelCase :       List[str]     					=    {
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}




class      SCREAMING_SNAKE_CASE__  (			UpperCamelCase__      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      "ernie"



	def __init__(						self			:					List[Any]    ,						lowercase			:					Any=30_522    ,						lowercase			:					List[str]=768    ,						lowercase			:					Any=12    ,						lowercase			:					str=12    ,						lowercase			:					Optional[int]=3_072    ,						lowercase			:					List[Any]="gelu"    ,						lowercase			:					Union[str, Any]=0.1    ,						lowercase			:					List[str]=0.1    ,						lowercase			:					int=512    ,						lowercase			:					Optional[Any]=2    ,						lowercase			:					Optional[int]=3    ,						lowercase			:					Union[str, Any]=False    ,						lowercase			:					int=0.02    ,						lowercase			:					List[str]=1E-12    ,						lowercase			:					str=0    ,						lowercase			:					List[str]="absolute"    ,						lowercase			:					Any=True    ,						lowercase			:					Optional[Any]=None    ,						**lowercase			:					Optional[int]    ,						):


				'''simple docstring'''


				super().__init__(pad_token_id=snake_case_    ,						**snake_case_			)

				_snake_case						=    vocab_size
				_snake_case						=    hidden_size
				_snake_case						=    num_hidden_layers
				_snake_case						=    num_attention_heads
				_snake_case						=    hidden_act
				_snake_case						=    intermediate_size
				_snake_case						=    hidden_dropout_prob
				_snake_case						=    attention_probs_dropout_prob
				_snake_case						=    max_position_embeddings
				_snake_case						=    type_vocab_size
				_snake_case						=    task_type_vocab_size
				_snake_case						=    use_task_id
				_snake_case						=    initializer_range
				_snake_case						=    layer_norm_eps
				_snake_case						=    position_embedding_type
				_snake_case						=    use_cache
				_snake_case						=    classifier_dropout




class      SCREAMING_SNAKE_CASE__  (			UpperCamelCase__      ):





	'''simple docstring'''



	@property
	def 				A							(						self			:					int			):


				'''simple docstring'''


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

import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


_lowerCamelCase :       Optional[Any]     					=    [nn.LayerNorm]

_lowerCamelCase :       Optional[int]     					=    logging.get_logger(__name__)

_lowerCamelCase :       Optional[int]     					=    version.parse(version.parse(torch.__version__).base_version)

_lowerCamelCase :       Dict     					=    parsed_torch_version_base >= version.parse('''2.0''')
_lowerCamelCase :       str     					=    parsed_torch_version_base >= version.parse('''1.12''')
_lowerCamelCase :       str     					=    parsed_torch_version_base >= version.parse('''1.11''')
_lowerCamelCase :       Dict     					=    parsed_torch_version_base < version.parse('''1.11''')






def  a_	( __lowercase     :			Optional[int] ,      __lowercase     :			Optional[int] ,      __lowercase     :			Optional[Any] ,      __lowercase     :			Union[str, Any] ,      self     :			Optional[int]				)				->						Dict:
			from torch import _softmax_backward_data

			if is_torch_less_than_1_11:
						return _softmax_backward_data(__lowercase ,      __lowercase ,      parent.dim ,      self				)
			else:
						return _softmax_backward_data(__lowercase ,      __lowercase ,      parent.dim ,      self.dtype				)






def  a_	( __lowercase     :			nn.Linear ,      __lowercase     :			torch.LongTensor ,      __lowercase     :			int = 0				)				->						nn.Linear:
			_snake_case						=    index.to(layer.weight.device				)
			_snake_case						=    layer.weight.index_select(__lowercase ,      __lowercase				).clone().detach()
			if layer.bias is not None:
						if dim == 1:
									_snake_case						=    layer.bias.clone().detach()
						else:
									_snake_case						=    layer.bias[index].clone().detach()
			_snake_case						=    list(layer.weight.size()				)
			_snake_case						=    len(__lowercase				)
			_snake_case						=    nn.Linear(new_size[1] ,      new_size[0] ,      bias=layer.bias is not None				).to(layer.weight.device				)
			_snake_case						=    False
			new_layer.weight.copy_(W.contiguous()				)
			_snake_case						=    True
			if layer.bias is not None:
						_snake_case						=    False
						new_layer.bias.copy_(b.contiguous()				)
						_snake_case						=    True
			return new_layer




class      SCREAMING_SNAKE_CASE__  (			nn.Module      ):





	'''simple docstring'''



	def __init__(						self			:					Optional[Any]    ,						lowercase			:					List[str]    ,						lowercase			:					Tuple			):


				'''simple docstring'''


				super().__init__()
				_snake_case						=    nf
				_snake_case						=    nn.Parameter(torch.empty(lowercase    ,						lowercase			)			)
				_snake_case						=    nn.Parameter(torch.zeros(lowercase			)			)
				nn.init.normal_(self.weight    ,						std=0.02			)





	def 				A							(						self			:					List[Any]    ,						lowercase			:					int			):


				'''simple docstring'''


				_snake_case						=    x.size()[:-1] + (self.nf,)
				_snake_case						=    torch.addmm(self.bias    ,						x.view(-1    ,						x.size(-1			)			)    ,						self.weight			)
				_snake_case						=    x.view(lowercase			)
				return x






def  a_	( __lowercase     :			ConvaD ,      __lowercase     :			torch.LongTensor ,      __lowercase     :			int = 1				)				->						ConvaD:
			_snake_case						=    index.to(layer.weight.device				)
			_snake_case						=    layer.weight.index_select(__lowercase ,      __lowercase				).clone().detach()
			if dim == 0:
						_snake_case						=    layer.bias.clone().detach()
			else:
						_snake_case						=    layer.bias[index].clone().detach()
			_snake_case						=    list(layer.weight.size()				)
			_snake_case						=    len(__lowercase				)
			_snake_case						=    ConvaD(new_size[1] ,      new_size[0]				).to(layer.weight.device				)
			_snake_case						=    False
			new_layer.weight.copy_(W.contiguous()				)
			_snake_case						=    True
			_snake_case						=    False
			new_layer.bias.copy_(b.contiguous()				)
			_snake_case						=    True
			return new_layer






def  a_	( __lowercase     :			Union[nn.Linear, ConvaD] ,      __lowercase     :			torch.LongTensor ,      __lowercase     :			Optional[int] = None				)				->						Union[nn.Linear, ConvaD]:
			if isinstance(__lowercase ,      nn.Linear				):
						return prune_linear_layer(__lowercase ,      __lowercase ,      dim=0 if dim is None else dim				)
			elif isinstance(__lowercase ,      __lowercase				):
						return prune_convad_layer(__lowercase ,      __lowercase ,      dim=1 if dim is None else dim				)
			else:
						raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''				)






def  a_	( __lowercase     :			Callable[..., torch.Tensor] ,      __lowercase     :			int ,      __lowercase     :			int ,      *__lowercase     :			List[str]				)				->						torch.Tensor:
			assert len(__lowercase				) > 0, f'''{input_tensors} has to be a tuple/list of tensors'''

			# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
			_snake_case						=    len(inspect.signature(__lowercase				).parameters				)
			if num_args_in_forward_chunk_fn != len(__lowercase				):
						raise ValueError(
						    f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(__lowercase				)} input '''
						    'tensors are given'				)

			if chunk_size > 0:
						_snake_case						=    input_tensors[0].shape[chunk_dim]
						for input_tensor in input_tensors:
									if input_tensor.shape[chunk_dim] != tensor_shape:
												raise ValueError(
												    f'''All input tenors have to be of the same shape: {tensor_shape}, '''
												    f'''found shape {input_tensor.shape[chunk_dim]}'''				)

						if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
									raise ValueError(
									    f'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
									    f'''size {chunk_size}'''				)

						_snake_case						=    input_tensors[0].shape[chunk_dim] // chunk_size

						# chunk input tensor into tuples
						_snake_case						=    tuple(input_tensor.chunk(__lowercase ,      dim=__lowercase				) for input_tensor in input_tensors				)
						# apply forward fn to every tuple
						_snake_case						=    tuple(forward_fn(*__lowercase				) for input_tensors_chunk in zip(*__lowercase				)				)
						# concatenate output at same dimension
						return torch.cat(__lowercase ,      dim=__lowercase				)

			return forward_fn(*__lowercase				)






def  a_	( __lowercase     :			List[int] ,      __lowercase     :			int ,      __lowercase     :			int ,      __lowercase     :			Set[int]				)				->						Tuple[Set[int], torch.LongTensor]:
			_snake_case						=    torch.ones(__lowercase ,      __lowercase				)
			_snake_case						=    set(__lowercase				) - already_pruned_heads  # Convert to set and remove already pruned heads
			for head in heads:
						# Compute how many pruned heads are before the head and move the index accordingly
						_snake_case						=    head - sum(1 if h < head else 0 for h in already_pruned_heads				)
						_snake_case						=    0
			_snake_case						=    mask.view(-1				).contiguous().eq(1				)
			_snake_case						=    torch.arange(len(__lowercase				)				)[mask].long()
			return heads, index






def  a_	( *__lowercase     :			Union[torch.Tensor, List[torch.Tensor]] ,      __lowercase     :			Optional[str] = None				)				->						Tuple[torch.Tensor, ...]:
			return torch.meshgrid(*__lowercase ,      indexing=__lowercase				)






def  a_	( __lowercase     :			torch.Tensor				)				->						Tuple[torch.device, int, int]:
			return tensor.device, storage_ptr(__lowercase				), storage_size(__lowercase				)