
"""simple docstring"""


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								=						[nn.LayerNorm]

lowerCAmelCase								=						logging.get_logger(__name__)

lowerCAmelCase								=						version.parse(version.parse(torch.__version__).base_version)

lowerCAmelCase								=						parsed_torch_version_base >= version.parse("""2.0""")
lowerCAmelCase								=						parsed_torch_version_base >= version.parse("""1.12""")
lowerCAmelCase								=						parsed_torch_version_base >= version.parse("""1.11""")
lowerCAmelCase								=						parsed_torch_version_base < version.parse("""1.11""")






def        lowerCAmelCase_       (		snake_case_		:      Union[str, Any]							,   snake_case_		:      Any							,   snake_case_		:      str							,   snake_case_		:      Optional[int]							,   self		:      int      )       ->Optional[int]:
			from torch import _softmax_backward_data

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






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

class    A_     (					nn.Module       ):



							"""simple docstring"""






							def __init__(  self  :int					,							lowerCamelCase_  :str					,							lowerCamelCase_  :Dict	):





										"""simple docstring"""

										super().__init__()
										lowerCamelCase__     :					List[str]		=nf
										lowerCamelCase__     :					str		=nn.Parameter(torch.empty(lowerCamelCase_					,							lowerCamelCase_	)	)
										lowerCamelCase__     :					Union[str, Any]		=nn.Parameter(torch.zeros(lowerCamelCase_	)	)
										nn.init.normal_(self.weight					,							std=0.02	)





							def 				UpperCAmelCase__							(  self  :List[Any]					,							lowerCamelCase_  :Dict	):





										"""simple docstring"""

										lowerCamelCase__     :					List[str]		=x.size()[:-1] + (self.nf,)
										lowerCamelCase__     :					str		=torch.addmm(self.bias					,							x.view(-1					,							x.size(-1	)	)					,							self.weight	)
										lowerCamelCase__     :					Any		=x.view(lowerCamelCase_	)
										return x






def        lowerCAmelCase_       (		snake_case_		:      ConvaD							,   snake_case_		:      torch.LongTensor							,   snake_case_		:      int = 1      )       ->ConvaD:
			lowerCamelCase__     :					List[str]		=index.to(layer.weight.device      )
			lowerCamelCase__     :					Dict		=layer.weight.index_select(snake_case_							,   snake_case_      ).clone().detach()
			if dim == 0:
						lowerCamelCase__     :					Dict		=layer.bias.clone().detach()
			else:
						lowerCamelCase__     :					Optional[int]		=layer.bias[index].clone().detach()
			lowerCamelCase__     :					str		=list(layer.weight.size()      )
			lowerCamelCase__     :					Any		=len(snake_case_      )
			lowerCamelCase__     :					str		=ConvaD(new_size[1]							,   new_size[0]      ).to(layer.weight.device      )
			lowerCamelCase__     :					Dict		=False
			new_layer.weight.copy_(W.contiguous()      )
			lowerCamelCase__     :					Optional[Any]		=True
			lowerCamelCase__     :					List[Any]		=False
			new_layer.bias.copy_(b.contiguous()      )
			lowerCamelCase__     :					Union[str, Any]		=True
			return new_layer






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






def        lowerCAmelCase_       (		snake_case_		:      Callable[..., torch.Tensor]							,   snake_case_		:      int							,   snake_case_		:      int							,   *snake_case_		:      Any      )       ->torch.Tensor:
			assert len(snake_case_      ) > 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
			lowerCamelCase__     :					Tuple		=len(inspect.signature(snake_case_      ).parameters      )
			if num_args_in_forward_chunk_fn != len(snake_case_      ):
						raise ValueError(
						    f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(snake_case_      )} input """
						    'tensors are given'      )

			if chunk_size > 0:
						lowerCamelCase__     :					Any		=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}"""      )

						lowerCamelCase__     :					str		=input_tensors[0].shape[chunk_dim] // chunk_size

						# chunk input tensor into tuples
						lowerCamelCase__     :					Optional[Any]		=tuple(input_tensor.chunk(snake_case_							,   dim=snake_case_      ) for input_tensor in input_tensors      )
						# apply forward fn to every tuple
						lowerCamelCase__     :					List[str]		=tuple(forward_fn(*snake_case_      ) for input_tensors_chunk in zip(*snake_case_      )      )
						# concatenate output at same dimension
						return torch.cat(snake_case_							,   dim=snake_case_      )

			return forward_fn(*snake_case_      )






def        lowerCAmelCase_       (		snake_case_		:      List[int]							,   snake_case_		:      int							,   snake_case_		:      int							,   snake_case_		:      Set[int]      )       ->Tuple[Set[int], torch.LongTensor]:
			lowerCamelCase__     :					Union[str, Any]		=torch.ones(snake_case_							,   snake_case_      )
			lowerCamelCase__     :					Optional[int]		=set(snake_case_      ) - 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
						lowerCamelCase__     :					List[Any]		=head - sum(1 if h < head else 0 for h in already_pruned_heads      )
						lowerCamelCase__     :					List[str]		=0
			lowerCamelCase__     :					Any		=mask.view(-1      ).contiguous().eq(1      )
			lowerCamelCase__     :					torch.LongTensor		=torch.arange(len(snake_case_      )      )[mask].long()
			return heads, index






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






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

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase								=						{
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["""DebertaV2TokenizerFast"""]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFDebertaV2ForMaskedLM""",
							    """TFDebertaV2ForQuestionAnswering""",
							    """TFDebertaV2ForSequenceClassification""",
							    """TFDebertaV2ForTokenClassification""",
							    """TFDebertaV2Model""",
							    """TFDebertaV2PreTrainedModel""",
							]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DebertaV2ForMaskedLM""",
							    """DebertaV2ForMultipleChoice""",
							    """DebertaV2ForQuestionAnswering""",
							    """DebertaV2ForSequenceClassification""",
							    """DebertaV2ForTokenClassification""",
							    """DebertaV2Model""",
							    """DebertaV2PreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_deberta_va import (
							    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DebertaVaConfig,
							    DebertaVaOnnxConfig,
							)
							from .tokenization_deberta_va import DebertaVaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta_va import (
														    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaVaForMaskedLM,
														    TFDebertaVaForQuestionAnswering,
														    TFDebertaVaForSequenceClassification,
														    TFDebertaVaForTokenClassification,
														    TFDebertaVaModel,
														    TFDebertaVaPreTrainedModel,
														)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta_va import (
														    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaVaForMaskedLM,
														    DebertaVaForMultipleChoice,
														    DebertaVaForQuestionAnswering,
														    DebertaVaForSequenceClassification,
														    DebertaVaForTokenClassification,
														    DebertaVaModel,
														    DebertaVaPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase								=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)