




import torch
from torch import nn


class    lowerCamelCase    (		nn.Module						):






			'''simple docstring'''





			def __init__( self			:   int       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   Union[str, Any]=1       ,						lowerCamelCase_			:   str=False  )						->   List[str]:
								super().__init__()

								__magic_name__							:	Dict    							=							n_token
								__magic_name__							:	Any    							=							d_embed
								__magic_name__							:	Union[str, Any]    							=							d_proj

								__magic_name__							:	Any    							=							cutoffs + [n_token]
								__magic_name__							:	Union[str, Any]    							=							[0] + self.cutoffs
								__magic_name__							:	Union[str, Any]    							=							div_val

								__magic_name__							:	Any    							=							self.cutoffs[0]
								__magic_name__							:	Any    							=							len(self.cutoffs  ) - 1
								__magic_name__							:	Any    							=							self.shortlist_size + self.n_clusters

								if self.n_clusters > 0:
													__magic_name__							:	Optional[Any]    							=							nn.Parameter(torch.zeros(self.n_clusters       ,						self.d_embed  )  )
													__magic_name__							:	Tuple    							=							nn.Parameter(torch.zeros(self.n_clusters  )  )

								__magic_name__							:	Union[str, Any]    							=							nn.ModuleList()
								__magic_name__							:	Union[str, Any]    							=							nn.ParameterList()

								if div_val == 1:
													for i in range(len(self.cutoffs  )  ):
																		if d_proj != d_embed:
																							self.out_projs.append(nn.Parameter(torch.FloatTensor(lowerCamelCase_       ,						lowerCamelCase_  )  )  )
																		else:
																							self.out_projs.append(lowerCamelCase_  )

													self.out_layers.append(nn.Linear(lowerCamelCase_       ,						lowerCamelCase_  )  )
								else:
													for i in range(len(self.cutoffs  )  ):
																		__magic_name__							,    __magic_name__							:	int    							=							self.cutoff_ends[i], self.cutoff_ends[i + 1]
																		__magic_name__							:	int    							=							d_embed // (div_val**i)

																		self.out_projs.append(nn.Parameter(torch.FloatTensor(lowerCamelCase_       ,						lowerCamelCase_  )  )  )

																		self.out_layers.append(nn.Linear(lowerCamelCase_       ,						r_idx - l_idx  )  )

								__magic_name__							:	List[Any]    							=							keep_order

			def 							UpperCAmelCase__       ( self			:   Tuple       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   str  )						->   str:
								if proj is None:
													__magic_name__							:	Dict    							=							nn.functional.linear(lowerCamelCase_       ,						lowerCamelCase_       ,						bias=lowerCamelCase_  )
								else:
													# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
													__magic_name__							:	Optional[int]    							=							nn.functional.linear(lowerCamelCase_       ,						proj.t().contiguous()  )
													__magic_name__							:	Optional[int]    							=							nn.functional.linear(lowerCamelCase_       ,						lowerCamelCase_       ,						bias=lowerCamelCase_  )
													# else:
													#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
													#     if bias is not None:
													#         logit = logit + bias

								return logit

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   str=None       ,						lowerCamelCase_			:   List[Any]=False  )						->   Optional[int]:

								if labels is not None:
													# Shift so that tokens < n predict n
													__magic_name__							:	Tuple    							=							hidden[..., :-1, :].contiguous()
													__magic_name__							:	str    							=							labels[..., 1:].contiguous()
													__magic_name__							:	List[Any]    							=							hidden.view(-1       ,						hidden.size(-1  )  )
													__magic_name__							:	str    							=							labels.view(-1  )
													if hidden.size(0  ) != labels.size(0  ):
																		raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''  )
								else:
													__magic_name__							:	int    							=							hidden.view(-1       ,						hidden.size(-1  )  )

								if self.n_clusters == 0:
													__magic_name__							:	Optional[int]    							=							self._compute_logit(lowerCamelCase_       ,						self.out_layers[0].weight       ,						self.out_layers[0].bias       ,						self.out_projs[0]  )
													if labels is not None:
																		__magic_name__							:	Union[str, Any]    							=							labels != -100
																		__magic_name__							:	Union[str, Any]    							=							torch.zeros_like(lowerCamelCase_       ,						dtype=hidden.dtype       ,						device=hidden.device  )
																		__magic_name__							:	Any    							=							(
																		    -nn.functional.log_softmax(lowerCamelCase_       ,						dim=-1  )[mask].gather(1       ,						labels[mask].unsqueeze(1  )  ).squeeze(1  )
																		)
													else:
																		__magic_name__							:	Any    							=							nn.functional.log_softmax(lowerCamelCase_       ,						dim=-1  )
								else:
													# construct weights and biases
													__magic_name__							,    __magic_name__							:	Any    							=							[], []
													for i in range(len(self.cutoffs  )  ):
																		if self.div_val == 1:
																							__magic_name__							,    __magic_name__							:	List[str]    							=							self.cutoff_ends[i], self.cutoff_ends[i + 1]
																							__magic_name__							:	Tuple    							=							self.out_layers[0].weight[l_idx:r_idx]
																							__magic_name__							:	str    							=							self.out_layers[0].bias[l_idx:r_idx]
																		else:
																							__magic_name__							:	Any    							=							self.out_layers[i].weight
																							__magic_name__							:	List[Any]    							=							self.out_layers[i].bias

																		if i == 0:
																							__magic_name__							:	str    							=							torch.cat([weight_i, self.cluster_weight]       ,						dim=0  )
																							__magic_name__							:	Union[str, Any]    							=							torch.cat([bias_i, self.cluster_bias]       ,						dim=0  )

																		weights.append(lowerCamelCase_  )
																		biases.append(lowerCamelCase_  )

													__magic_name__							,    __magic_name__							,    __magic_name__							:	int    							=							weights[0], biases[0], self.out_projs[0]

													__magic_name__							:	str    							=							self._compute_logit(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
													__magic_name__							:	Union[str, Any]    							=							nn.functional.log_softmax(lowerCamelCase_       ,						dim=1  )

													if labels is None:
																		__magic_name__							:	Tuple    							=							hidden.new_empty((head_logit.size(0  ), self.n_token)  )
													else:
																		__magic_name__							:	List[str]    							=							torch.zeros_like(lowerCamelCase_       ,						dtype=hidden.dtype       ,						device=hidden.device  )

													__magic_name__							:	Optional[Any]    							=							0
													__magic_name__							:	Union[str, Any]    							=							[0] + self.cutoffs
													for i in range(len(lowerCamelCase_  ) - 1  ):
																		__magic_name__							,    __magic_name__							:	List[str]    							=							cutoff_values[i], cutoff_values[i + 1]

																		if labels is not None:
																							__magic_name__							:	Tuple    							=							(labels >= l_idx) & (labels < r_idx)
																							__magic_name__							:	Tuple    							=							mask_i.nonzero().squeeze()

																							if indices_i.numel() == 0:
																												continue

																							__magic_name__							:	List[Any]    							=							labels.index_select(0       ,						lowerCamelCase_  ) - l_idx
																							__magic_name__							:	Tuple    							=							head_logprob.index_select(0       ,						lowerCamelCase_  )
																							__magic_name__							:	Dict    							=							hidden.index_select(0       ,						lowerCamelCase_  )
																		else:
																							__magic_name__							:	List[Any]    							=							hidden

																		if i == 0:
																							if labels is not None:
																												__magic_name__							:	str    							=							head_logprob_i.gather(1       ,						target_i[:, None]  ).squeeze(1  )
																							else:
																												__magic_name__							:	List[Any]    							=							head_logprob[:, : self.cutoffs[0]]
																		else:
																							__magic_name__							,    __magic_name__							,    __magic_name__							:	int    							=							weights[i], biases[i], self.out_projs[i]

																							__magic_name__							:	List[str]    							=							self._compute_logit(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
																							__magic_name__							:	List[Any]    							=							nn.functional.log_softmax(lowerCamelCase_       ,						dim=1  )
																							__magic_name__							:	Optional[int]    							=							self.cutoffs[0] + i - 1  # No probability for the head cluster
																							if labels is not None:
																												__magic_name__							:	Union[str, Any]    							=							head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																												    1       ,						target_i[:, None]  ).squeeze(1  )
																							else:
																												__magic_name__							:	Union[str, Any]    							=							head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																												__magic_name__							:	Any    							=							logprob_i

																		if labels is not None:
																							if (hasattr(self       ,						'''keep_order'''  ) and self.keep_order) or keep_order:
																												out.index_copy_(0       ,						lowerCamelCase_       ,						-logprob_i  )
																							else:
																												out[offset : offset + logprob_i.size(0  )].copy_(-logprob_i  )
																							offset += logprob_i.size(0  )

								return out


			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[int]  )						->   List[str]:
								if self.n_clusters == 0:
													__magic_name__							:	List[Any]    							=							self._compute_logit(lowerCamelCase_       ,						self.out_layers[0].weight       ,						self.out_layers[0].bias       ,						self.out_projs[0]  )
													return nn.functional.log_softmax(lowerCamelCase_       ,						dim=-1  )
								else:
													# construct weights and biases
													__magic_name__							,    __magic_name__							:	Optional[int]    							=							[], []
													for i in range(len(self.cutoffs  )  ):
																		if self.div_val == 1:
																							__magic_name__							,    __magic_name__							:	Union[str, Any]    							=							self.cutoff_ends[i], self.cutoff_ends[i + 1]
																							__magic_name__							:	Any    							=							self.out_layers[0].weight[l_idx:r_idx]
																							__magic_name__							:	List[Any]    							=							self.out_layers[0].bias[l_idx:r_idx]
																		else:
																							__magic_name__							:	str    							=							self.out_layers[i].weight
																							__magic_name__							:	Any    							=							self.out_layers[i].bias

																		if i == 0:
																							__magic_name__							:	List[Any]    							=							torch.cat([weight_i, self.cluster_weight]       ,						dim=0  )
																							__magic_name__							:	Optional[Any]    							=							torch.cat([bias_i, self.cluster_bias]       ,						dim=0  )

																		weights.append(lowerCamelCase_  )
																		biases.append(lowerCamelCase_  )

													__magic_name__							,    __magic_name__							,    __magic_name__							:	int    							=							weights[0], biases[0], self.out_projs[0]
													__magic_name__							:	Tuple    							=							self._compute_logit(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )

													__magic_name__							:	Optional[Any]    							=							hidden.new_empty((head_logit.size(0  ), self.n_token)  )
													__magic_name__							:	List[str]    							=							nn.functional.log_softmax(lowerCamelCase_       ,						dim=1  )

													__magic_name__							:	Dict    							=							[0] + self.cutoffs
													for i in range(len(lowerCamelCase_  ) - 1  ):
																		__magic_name__							,    __magic_name__							:	int    							=							cutoff_values[i], cutoff_values[i + 1]

																		if i == 0:
																							__magic_name__							:	List[str]    							=							head_logprob[:, : self.cutoffs[0]]
																		else:
																							__magic_name__							,    __magic_name__							,    __magic_name__							:	List[str]    							=							weights[i], biases[i], self.out_projs[i]

																							__magic_name__							:	Any    							=							self._compute_logit(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
																							__magic_name__							:	List[str]    							=							nn.functional.log_softmax(lowerCamelCase_       ,						dim=1  )

																							__magic_name__							:	Any    							=							head_logprob[:, -i] + tail_logprob_i
																							__magic_name__							:	int    							=							logprob_i

													return out









# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__lowerCamelCase   :						int				=			logging.get_logger(__name__)

__lowerCamelCase   :						Dict[Optional[str], Type[Formatter]]				=			{}
__lowerCamelCase   :						Dict[Optional[str], str]				=			{}
__lowerCamelCase   :						Dict[Optional[str], Exception]				=			{}







def 		lowercase__   (		__A:	type							,__A:	Optional[str]							,__A:	Optional[List[str]] = None							,):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							aliases if aliases is not None else []
					if format_type in _FORMAT_TYPES:
										logger.warning(
										    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''				)
					__magic_name__							:	Tuple    							=							formatter_cls
					for alias in set(aliases + [format_type]				):
										if alias in _FORMAT_TYPES_ALIASES:
															logger.warning(
															    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''				)
										__magic_name__							:	Optional[int]    							=							format_type







def 		lowercase__   (		__A:	Exception							,__A:	Optional[str]							,__A:	Optional[List[str]] = None				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							aliases if aliases is not None else []
					for alias in set(aliases + [format_type]				):
										__magic_name__							:	List[str]    							=							unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
	__lowerCamelCase   :						str				=			ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
	_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
	__lowerCamelCase   :						int				=			ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
	_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
	__lowerCamelCase   :						str				=			ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])







def 		lowercase__   (		__A:	Optional[str]				):





					'''simple docstring'''






					if format_type in _FORMAT_TYPES_ALIASES:
										return _FORMAT_TYPES_ALIASES[format_type]
					else:
										return format_type







def 		lowercase__   (		__A:	Optional[str]							,**__A:	Any				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							get_format_type_from_alias(__A				)
					if format_type in _FORMAT_TYPES:
										return _FORMAT_TYPES[format_type](**__A				)
					if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
										raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
					else:
										raise ValueError(
										    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None				)}, but got \'{format_type}\''''				)



