






'''simple docstring'''







import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


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

_A	:			int         ={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A	:			Tuple         ={
    '''vocab_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
_A	:			Dict         ={
    '''vocab_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
_A	:			Any         ={
    '''vocab_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}

_A	:			Optional[int]         ={
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
_A	:			Any         ={
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
_A	:			Union[str, Any]         ={
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


_A	:			str         ={
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
_A	:			Any         ={
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
_A	:			Dict         ={
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}

class _lowercase	(				_lowercase		):
							a				      =       VOCAB_FILES_NAMES
							a				      =       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							a				      =       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a				      =       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class _lowercase	(				_lowercase		):
							a				      =       VOCAB_FILES_NAMES
							a				      =       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							a				      =       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a				      =       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



_A	:			int         =collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

_A	:			Any         =collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


_A	:			str         =r'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''

@add_start_docstrings(_lowercase		)
class _lowercase	:




							def __call__(							self:				List[str]			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				Optional[str] = None			,							UpperCamelCase__:				Optional[str] = None			,							UpperCamelCase__:				Union[bool, str] = False			,							UpperCamelCase__:				Union[bool, str] = False			,							UpperCamelCase__:				Optional[int] = None			,							UpperCamelCase__:				Optional[Union[str, TensorType]] = None			,							UpperCamelCase__:				Optional[bool] = None			,							**UpperCamelCase__:				int			,							):
														if titles is None and texts is None:
																					return super().__call__(
																					    UpperCamelCase__			,							padding=UpperCamelCase__			,							truncation=UpperCamelCase__			,							max_length=UpperCamelCase__			,							return_tensors=UpperCamelCase__			,							return_attention_mask=UpperCamelCase__			,							**UpperCamelCase__			,							)
														elif titles is None or texts is None:
																					lowerCamelCase__					:	List[str]         =       titles if texts is None else texts
																					return super().__call__(
																					    UpperCamelCase__			,							UpperCamelCase__			,							padding=UpperCamelCase__			,							truncation=UpperCamelCase__			,							max_length=UpperCamelCase__			,							return_tensors=UpperCamelCase__			,							return_attention_mask=UpperCamelCase__			,							**UpperCamelCase__			,							)
														lowerCamelCase__					:	Union[str, Any]         =       titles if not isinstance(UpperCamelCase__			,							UpperCamelCase__     ) else [titles]
														lowerCamelCase__					:	Any         =       texts if not isinstance(UpperCamelCase__			,							UpperCamelCase__     ) else [texts]
														lowerCamelCase__					:	str         =       len(UpperCamelCase__     )
														lowerCamelCase__					:	Any         =       questions if not isinstance(UpperCamelCase__			,							UpperCamelCase__     ) else [questions] * n_passages
														if len(UpperCamelCase__     ) != len(UpperCamelCase__     ):
																					raise ValueError(
																					    F'''There should be as many titles than texts but got {len(UpperCamelCase__     )} titles and {len(UpperCamelCase__     )} texts.'''     )
														lowerCamelCase__					:	Optional[Any]         =       super().__call__(UpperCamelCase__			,							UpperCamelCase__			,							padding=UpperCamelCase__			,							truncation=UpperCamelCase__     )["""input_ids"""]
														lowerCamelCase__					:	Union[str, Any]         =       super().__call__(UpperCamelCase__			,							add_special_tokens=UpperCamelCase__			,							padding=UpperCamelCase__			,							truncation=UpperCamelCase__     )["""input_ids"""]
														lowerCamelCase__					:	Any         =       {
														    """input_ids""": [
														        (encoded_question_and_title + encoded_text)[:max_length]
														        if max_length is not None and truncation
														        else encoded_question_and_title + encoded_text
														        for encoded_question_and_title, encoded_text in zip(UpperCamelCase__			,							UpperCamelCase__     )
														    ]
														}
														if return_attention_mask is not False:
																					lowerCamelCase__					:	Optional[Any]         =       []
																					for input_ids in encoded_inputs["input_ids"]:
																												attention_mask.append([int(input_id != self.pad_token_id     ) for input_id in input_ids]     )
																					lowerCamelCase__					:	List[Any]         =       attention_mask
														return self.pad(UpperCamelCase__			,							padding=UpperCamelCase__			,							max_length=UpperCamelCase__			,							return_tensors=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				BatchEncoding			,							UpperCamelCase__:				DPRReaderOutput			,							UpperCamelCase__:				int = 16			,							UpperCamelCase__:				int = 64			,							UpperCamelCase__:				int = 4			,							):
														lowerCamelCase__					:	Any         =       reader_input["""input_ids"""]
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	int         =       reader_output[:3]
														lowerCamelCase__					:	List[str]         =       len(UpperCamelCase__     )
														lowerCamelCase__					:	List[Any]         =       sorted(range(UpperCamelCase__     )			,							reverse=UpperCamelCase__			,							key=relevance_logits.__getitem__     )
														lowerCamelCase__					:	List[DPRReaderOutput]         =       []
														for doc_id in sorted_docs:
																					lowerCamelCase__					:	Union[str, Any]         =       list(input_ids[doc_id]     )
																					# assuming question & title information is at the beginning of the sequence
																					lowerCamelCase__					:	Optional[Any]         =       sequence_ids.index(self.sep_token_id			,							2     ) + 1  # second sep id
																					if sequence_ids[-1] == self.pad_token_id:
																												lowerCamelCase__					:	Union[str, Any]         =       sequence_ids.index(self.pad_token_id     )
																					else:
																												lowerCamelCase__					:	Dict         =       len(UpperCamelCase__     )

																					lowerCamelCase__					:	Union[str, Any]         =       self._get_best_spans(
																					    start_logits=start_logits[doc_id][passage_offset:sequence_len]			,							end_logits=end_logits[doc_id][passage_offset:sequence_len]			,							max_answer_length=UpperCamelCase__			,							top_spans=UpperCamelCase__			,							)
																					for start_index, end_index in best_spans:
																												start_index += passage_offset
																												end_index += passage_offset
																												nbest_spans_predictions.append(
																												    DPRSpanPrediction(
																												        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]			,							relevance_score=relevance_logits[doc_id]			,							doc_id=UpperCamelCase__			,							start_index=UpperCamelCase__			,							end_index=UpperCamelCase__			,							text=self.decode(sequence_ids[start_index : end_index + 1]     )			,							)     )
																					if len(UpperCamelCase__     ) >= num_spans:
																												break
														return nbest_spans_predictions[:num_spans]




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				List[int]			,							UpperCamelCase__:				List[int]			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int			,							):
														lowerCamelCase__					:	List[str]         =       []
														for start_index, start_score in enumerate(UpperCamelCase__     ):
																					for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]     ):
																												scores.append(((start_index, start_index + answer_length), start_score + end_score)     )
														lowerCamelCase__					:	Optional[Any]         =       sorted(UpperCamelCase__			,							key=lambda UpperCamelCase__     : x[1]			,							reverse=UpperCamelCase__     )
														lowerCamelCase__					:	List[Any]         =       []
														for (start_index, end_index), score in scores:
																					if start_index > end_index:
																												raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]'''     )
																					lowerCamelCase__					:	List[str]         =       end_index - start_index + 1
																					if length > max_answer_length:
																												raise ValueError(F'''Span is too long: {length} > {max_answer_length}'''     )
																					if any(
																					    start_index <= prev_start_index <= prev_end_index <= end_index
																					    or prev_start_index <= start_index <= end_index <= prev_end_index
																					    for (prev_start_index, prev_end_index) in chosen_span_intervals     ):
																												continue
																					chosen_span_intervals.append((start_index, end_index)     )

																					if len(UpperCamelCase__     ) == top_spans:
																												break
														return chosen_span_intervals



@add_end_docstrings(_lowercase		)
class _lowercase	(				_lowercase				,      _lowercase		):
							a				      =       VOCAB_FILES_NAMES
							a				      =       READER_PRETRAINED_VOCAB_FILES_MAP
							a				      =       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a				      =       READER_PRETRAINED_INIT_CONFIGURATION
							a				      =       ["""input_ids""", """attention_mask"""]



"""simple docstring"""





import torch
from torch import nn






class     _UpperCAmelCase(    nn.Module	):



		def __init__(					self				,		__a				,		__a				,		__a				,		__a				,		__a=1				,		__a=False)       ->       Tuple:






							'''simple docstring'''
							super().__init__()

							_UpperCamelCase							 =  n_token
							_UpperCamelCase							 =  d_embed
							_UpperCamelCase							 =  d_proj

							_UpperCamelCase							 =  cutoffs + [n_token]
							_UpperCamelCase							 =  [0] + self.cutoffs
							_UpperCamelCase							 =  div_val

							_UpperCamelCase							 =  self.cutoffs[0]
							_UpperCamelCase							 =  len(self.cutoffs) - 1
							_UpperCamelCase							 =  self.shortlist_size + self.n_clusters

							if self.n_clusters > 0:
												_UpperCamelCase							 =  nn.Parameter(torch.zeros(self.n_clusters				,		self.d_embed))
												_UpperCamelCase							 =  nn.Parameter(torch.zeros(self.n_clusters))

							_UpperCamelCase							 =  nn.ModuleList()
							_UpperCamelCase							 =  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(__a				,		__a)))
																	else:
																						self.out_projs.append(__a)

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

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

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

							_UpperCamelCase							 =  keep_order



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a)       ->       Any:






							'''simple docstring'''
							if proj is None:
												_UpperCamelCase							 =  nn.functional.linear(__a				,		__a				,		bias=__a)
							else:
												# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
												_UpperCamelCase							 =  nn.functional.linear(__a				,		proj.t().contiguous())
												_UpperCamelCase							 =  nn.functional.linear(__a				,		__a				,		bias=__a)
												# 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				,		__a				,		__a=None				,		__a=False)       ->       Dict:






							'''simple docstring'''

							if labels is not None:
												# Shift so that tokens < n predict n
												_UpperCamelCase							 =  hidden[..., :-1, :].contiguous()
												_UpperCamelCase							 =  labels[..., 1:].contiguous()
												_UpperCamelCase							 =  hidden.view(-1				,		hidden.size(-1))
												_UpperCamelCase							 =  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:
												_UpperCamelCase							 =  hidden.view(-1				,		hidden.size(-1))

							if self.n_clusters == 0:
												_UpperCamelCase							 =  self._compute_logit(__a				,		self.out_layers[0].weight				,		self.out_layers[0].bias				,		self.out_projs[0])
												if labels is not None:
																	_UpperCamelCase							 =  labels != -1_00
																	_UpperCamelCase							 =  torch.zeros_like(__a				,		dtype=hidden.dtype				,		device=hidden.device)
																	_UpperCamelCase							 =  (
																	    -nn.functional.log_softmax(__a				,		dim=-1)[mask].gather(1				,		labels[mask].unsqueeze(1)).squeeze(1)
																	)
												else:
																	_UpperCamelCase							 =  nn.functional.log_softmax(__a				,		dim=-1)
							else:
												# construct weights and biases
												_UpperCamelCase ,  _UpperCamelCase							 =  [], []
												for i in range(len(self.cutoffs)):
																	if self.div_val == 1:
																						_UpperCamelCase ,  _UpperCamelCase							 =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						_UpperCamelCase							 =  self.out_layers[0].weight[l_idx:r_idx]
																						_UpperCamelCase							 =  self.out_layers[0].bias[l_idx:r_idx]
																	else:
																						_UpperCamelCase							 =  self.out_layers[i].weight
																						_UpperCamelCase							 =  self.out_layers[i].bias

																	if i == 0:
																						_UpperCamelCase							 =  torch.cat([weight_i, self.cluster_weight]				,		dim=0)
																						_UpperCamelCase							 =  torch.cat([bias_i, self.cluster_bias]				,		dim=0)

																	weights.append(__a)
																	biases.append(__a)

												_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  weights[0], biases[0], self.out_projs[0]

												_UpperCamelCase							 =  self._compute_logit(__a				,		__a				,		__a				,		__a)
												_UpperCamelCase							 =  nn.functional.log_softmax(__a				,		dim=1)

												if labels is None:
																	_UpperCamelCase							 =  hidden.new_empty((head_logit.size(0), self.n_token))
												else:
																	_UpperCamelCase							 =  torch.zeros_like(__a				,		dtype=hidden.dtype				,		device=hidden.device)

												_UpperCamelCase							 =  0
												_UpperCamelCase							 =  [0] + self.cutoffs
												for i in range(len(__a) - 1):
																	_UpperCamelCase ,  _UpperCamelCase							 =  cutoff_values[i], cutoff_values[i + 1]

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

																						if indices_i.numel() == 0:
																											continue

																						_UpperCamelCase							 =  labels.index_select(0				,		__a) - l_idx
																						_UpperCamelCase							 =  head_logprob.index_select(0				,		__a)
																						_UpperCamelCase							 =  hidden.index_select(0				,		__a)
																	else:
																						_UpperCamelCase							 =  hidden

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

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

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

							return out




		def 						UpperCAmelCase							(					self				,		__a)       ->       List[str]:






							'''simple docstring'''
							if self.n_clusters == 0:
												_UpperCamelCase							 =  self._compute_logit(__a				,		self.out_layers[0].weight				,		self.out_layers[0].bias				,		self.out_projs[0])
												return nn.functional.log_softmax(__a				,		dim=-1)
							else:
												# construct weights and biases
												_UpperCamelCase ,  _UpperCamelCase							 =  [], []
												for i in range(len(self.cutoffs)):
																	if self.div_val == 1:
																						_UpperCamelCase ,  _UpperCamelCase							 =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						_UpperCamelCase							 =  self.out_layers[0].weight[l_idx:r_idx]
																						_UpperCamelCase							 =  self.out_layers[0].bias[l_idx:r_idx]
																	else:
																						_UpperCamelCase							 =  self.out_layers[i].weight
																						_UpperCamelCase							 =  self.out_layers[i].bias

																	if i == 0:
																						_UpperCamelCase							 =  torch.cat([weight_i, self.cluster_weight]				,		dim=0)
																						_UpperCamelCase							 =  torch.cat([bias_i, self.cluster_bias]				,		dim=0)

																	weights.append(__a)
																	biases.append(__a)

												_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  weights[0], biases[0], self.out_projs[0]
												_UpperCamelCase							 =  self._compute_logit(__a				,		__a				,		__a				,		__a)

												_UpperCamelCase							 =  hidden.new_empty((head_logit.size(0), self.n_token))
												_UpperCamelCase							 =  nn.functional.log_softmax(__a				,		dim=1)

												_UpperCamelCase							 =  [0] + self.cutoffs
												for i in range(len(__a) - 1):
																	_UpperCamelCase ,  _UpperCamelCase							 =  cutoff_values[i], cutoff_values[i + 1]

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

																						_UpperCamelCase							 =  self._compute_logit(__a				,		__a				,		__a				,		__a)
																						_UpperCamelCase							 =  nn.functional.log_softmax(__a				,		dim=1)

																						_UpperCamelCase							 =  head_logprob[:, -i] + tail_logprob_i
																						_UpperCamelCase							 =  logprob_i

												return out






