import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				a							(			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:Any    			=		RobertaTokenizer
 lowerCamelCase	:str    			=		RobertaTokenizerFast
 lowerCamelCase	:List[Any]    			=		True
 lowerCamelCase	:List[Any]    			=		{'''cls_token''': '''<s>'''}



 def      UpperCAmelCase							(					self							)  ->					str:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        _A        =  [
            """l""",
            """o""",
            """w""",
            """e""",
            """r""",
            """s""",
            """t""",
            """i""",
            """d""",
            """n""",
            """\u0120""",
            """\u0120l""",
            """\u0120n""",
            """\u0120lo""",
            """\u0120low""",
            """er""",
            """\u0120lowest""",
            """\u0120newer""",
            """\u0120wider""",
            """<unk>""",
        ]
        _A        =  dict(zip(lowerCAmelCase_		,				range(len(lowerCAmelCase_							)							)							)							)
        _A        =  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
        _A        =  {"""unk_token""": """<unk>"""}

        _A        =  os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES["""vocab_file"""]							)
        _A        =  os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES["""merges_file"""]							)
        with open(self.vocab_file		,				"""w"""		,				encoding="""utf-8"""							) as fp:
               fp.write(json.dumps(lowerCAmelCase_							) + """\n"""							)
        with open(self.merges_file		,				"""w"""		,				encoding="""utf-8"""							) as fp:
               fp.write("""\n""".join(lowerCAmelCase_							)							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Tuple:
        kwargs.update(self.special_tokens_map							)
        return self.tokenizer_class.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        kwargs.update(self.special_tokens_map							)
        return RobertaTokenizerFast.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					str:
        _A        =  """lower newer"""
        _A        =  """lower newer"""
        return input_text, output_text



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  self.tokenizer_class(self.vocab_file		,				self.merges_file		,				**self.special_tokens_map							)
        _A        =  """lower newer"""
        _A        =  ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
        _A        =  tokenizer.tokenize(lowerCAmelCase_							)  # , add_prefix_space=True)
        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)

        _A        =  tokens + [tokenizer.unk_token]
        _A        =  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_							)		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  self.get_tokenizer()

        self.assertListEqual(tokenizer.encode("""Hello world!"""		,				add_special_tokens=lowerCAmelCase_							)		,				[0, 3_14_14, 2_32, 3_28, 2]							)
        self.assertListEqual(
            tokenizer.encode("""Hello world! cécé herlolip 418"""		,				add_special_tokens=lowerCAmelCase_							)		,				[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]		,				)



 @slow
 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  self.tokenizer_class.from_pretrained("""roberta-base"""							)

        _A        =  tokenizer.encode("""sequence builders"""		,				add_special_tokens=lowerCAmelCase_							)
        _A        =  tokenizer.encode("""multi-sequence build"""		,				add_special_tokens=lowerCAmelCase_							)

        _A        =  tokenizer.encode(
            """sequence builders"""		,				add_special_tokens=lowerCAmelCase_		,				add_prefix_space=lowerCAmelCase_							)
        _A        =  tokenizer.encode(
            """sequence builders"""		,				"""multi-sequence build"""		,				add_special_tokens=lowerCAmelCase_		,				add_prefix_space=lowerCAmelCase_							)

        _A        =  tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_							)
        _A        =  tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_		,				lowerCAmelCase_							)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.get_tokenizer()

        _A        =  """Encode this sequence."""
        _A        =  tokenizer.byte_encoder[""" """.encode("""utf-8"""							)[0]]

        # Testing encoder arguments
        _A        =  tokenizer.encode(lowerCAmelCase_		,				add_special_tokens=lowerCAmelCase_		,				add_prefix_space=lowerCAmelCase_							)
        _A        =  tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
        self.assertNotEqual(lowerCAmelCase_		,				lowerCAmelCase_							)

        _A        =  tokenizer.encode(lowerCAmelCase_		,				add_special_tokens=lowerCAmelCase_		,				add_prefix_space=lowerCAmelCase_							)
        _A        =  tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
        self.assertEqual(lowerCAmelCase_		,				lowerCAmelCase_							)

        tokenizer.add_special_tokens({"""bos_token""": """<s>"""}							)
        _A        =  tokenizer.encode(lowerCAmelCase_		,				add_special_tokens=lowerCAmelCase_							)
        _A        =  tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
        self.assertNotEqual(lowerCAmelCase_		,				lowerCAmelCase_							)

        # Testing spaces after special tokens
        _A        =  """<mask>"""
        tokenizer.add_special_tokens(
            {"""mask_token""": AddedToken(lowerCAmelCase_		,				lstrip=lowerCAmelCase_		,				rstrip=lowerCAmelCase_							)}							)  # mask token has a left space
        _A        =  tokenizer.convert_tokens_to_ids(lowerCAmelCase_							)

        _A        =  """Encode <mask> sequence"""
        _A        =  """Encode <mask>sequence"""

        _A        =  tokenizer.encode(lowerCAmelCase_							)
        _A        =  encoded.index(lowerCAmelCase_							)
        _A        =  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
        self.assertEqual(lowerCAmelCase_		,				lowerCAmelCase_							)

        _A        =  tokenizer.encode(lowerCAmelCase_							)
        _A        =  encoded.index(lowerCAmelCase_							)
        _A        =  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
        self.assertNotEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        pass



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
                      _A        =  self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_		,				**lowerCAmelCase_							)
                      _A        =  self.tokenizer_class.from_pretrained(lowerCAmelCase_		,				**lowerCAmelCase_							)
                      _A        =  """A, <mask> AllenNLP sentence."""
                      _A        =  tokenizer_r.encode_plus(lowerCAmelCase_		,				add_special_tokens=lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)
                      _A        =  tokenizer_p.encode_plus(lowerCAmelCase_		,				add_special_tokens=lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)

                      # token_type_ids should put 0 everywhere
                      self.assertEqual(sum(tokens_r["""token_type_ids"""]							)		,				sum(tokens_p["""token_type_ids"""]							)							)

                      # attention_mask should put 1 everywhere, so sum over length should be 1
                      self.assertEqual(
                          sum(tokens_r["""attention_mask"""]							) / len(tokens_r["""attention_mask"""]							)		,				sum(tokens_p["""attention_mask"""]							) / len(tokens_p["""attention_mask"""]							)		,				)

                      _A        =  tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
                      _A        =  tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]							)

                      # Rust correctly handles the space before the mask while python doesnt
                      self.assertSequenceEqual(tokens_p["""input_ids"""]		,				[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)
                      self.assertSequenceEqual(tokens_r["""input_ids"""]		,				[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)

                      self.assertSequenceEqual(
                          lowerCAmelCase_		,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)
                      self.assertSequenceEqual(
                          lowerCAmelCase_		,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        for trim_offsets, add_prefix_space in itertools.product([True, False]		,				repeat=2							):
               _A        =  self.rust_tokenizer_class.from_pretrained(
                   self.tmpdirname		,				use_fast=lowerCAmelCase_		,				add_prefix_space=lowerCAmelCase_		,				trim_offsets=lowerCAmelCase_							)

               _A        =  json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
               _A        =  json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

               self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]		,				lowerCAmelCase_							)

               self.assertEqual(post_processor_state["""add_prefix_space"""]		,				lowerCAmelCase_							)
               self.assertEqual(post_processor_state["""trim_offsets"""]		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
        # `trim_offsets`
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
                      _A        =  """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                      _A        =  F'''{text_of_1_token} {text_of_1_token}'''

                      _A        =  self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase_		,				use_fast=lowerCAmelCase_		,				add_prefix_space=lowerCAmelCase_		,				trim_offsets=lowerCAmelCase_							)
                      _A        =  tokenizer_r(lowerCAmelCase_		,				return_offsets_mapping=lowerCAmelCase_		,				add_special_tokens=lowerCAmelCase_							)
                      self.assertEqual(encoding.offset_mapping[0]		,				(0, len(lowerCAmelCase_							))							)
                      self.assertEqual(
                          encoding.offset_mapping[1]		,				(len(lowerCAmelCase_							) + 1, len(lowerCAmelCase_							) + 1 + len(lowerCAmelCase_							))		,				)

                      _A        =  self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase_		,				use_fast=lowerCAmelCase_		,				add_prefix_space=lowerCAmelCase_		,				trim_offsets=lowerCAmelCase_							)
                      _A        =  tokenizer_r(lowerCAmelCase_		,				return_offsets_mapping=lowerCAmelCase_		,				add_special_tokens=lowerCAmelCase_							)
                      self.assertEqual(encoding.offset_mapping[0]		,				(0, len(lowerCAmelCase_							))							)
                      self.assertEqual(
                          encoding.offset_mapping[1]		,				(len(lowerCAmelCase_							) + 1, len(lowerCAmelCase_							) + 1 + len(lowerCAmelCase_							))		,				)

                      _A        =  self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase_		,				use_fast=lowerCAmelCase_		,				add_prefix_space=lowerCAmelCase_		,				trim_offsets=lowerCAmelCase_							)
                      _A        =  tokenizer_r(lowerCAmelCase_		,				return_offsets_mapping=lowerCAmelCase_		,				add_special_tokens=lowerCAmelCase_							)
                      self.assertEqual(encoding.offset_mapping[0]		,				(0, len(lowerCAmelCase_							))							)
                      self.assertEqual(
                          encoding.offset_mapping[1]		,				(len(lowerCAmelCase_							), len(lowerCAmelCase_							) + 1 + len(lowerCAmelCase_							))		,				)

                      _A        =  self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase_		,				use_fast=lowerCAmelCase_		,				add_prefix_space=lowerCAmelCase_		,				trim_offsets=lowerCAmelCase_							)
                      _A        =  tokenizer_r(lowerCAmelCase_		,				return_offsets_mapping=lowerCAmelCase_		,				add_special_tokens=lowerCAmelCase_							)
                      self.assertEqual(encoding.offset_mapping[0]		,				(0, len(lowerCAmelCase_							))							)
                      self.assertEqual(
                          encoding.offset_mapping[1]		,				(len(lowerCAmelCase_							), len(lowerCAmelCase_							) + 1 + len(lowerCAmelCase_							))		,				)

                      _A        =  F''' {text}'''

                      # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                      #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                      # )
                      # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                      # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                      # self.assertEqual(
                      #     encoding.offset_mapping[1],
                      #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                      # )

                      _A        =  self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase_		,				use_fast=lowerCAmelCase_		,				add_prefix_space=lowerCAmelCase_		,				trim_offsets=lowerCAmelCase_							)
                      _A        =  tokenizer_r(lowerCAmelCase_		,				return_offsets_mapping=lowerCAmelCase_		,				add_special_tokens=lowerCAmelCase_							)
                      self.assertEqual(encoding.offset_mapping[0]		,				(1, 1 + len(lowerCAmelCase_							))							)
                      self.assertEqual(
                          encoding.offset_mapping[1]		,				(1 + len(lowerCAmelCase_							) + 1, 1 + len(lowerCAmelCase_							) + 1 + len(lowerCAmelCase_							))		,				)

                      _A        =  self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase_		,				use_fast=lowerCAmelCase_		,				add_prefix_space=lowerCAmelCase_		,				trim_offsets=lowerCAmelCase_							)
                      _A        =  tokenizer_r(lowerCAmelCase_		,				return_offsets_mapping=lowerCAmelCase_		,				add_special_tokens=lowerCAmelCase_							)
                      self.assertEqual(encoding.offset_mapping[0]		,				(0, 1 + len(lowerCAmelCase_							))							)
                      self.assertEqual(
                          encoding.offset_mapping[1]		,				(1 + len(lowerCAmelCase_							), 1 + len(lowerCAmelCase_							) + 1 + len(lowerCAmelCase_							))		,				)

                      _A        =  self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase_		,				use_fast=lowerCAmelCase_		,				add_prefix_space=lowerCAmelCase_		,				trim_offsets=lowerCAmelCase_							)
                      _A        =  tokenizer_r(lowerCAmelCase_		,				return_offsets_mapping=lowerCAmelCase_		,				add_special_tokens=lowerCAmelCase_							)
                      self.assertEqual(encoding.offset_mapping[0]		,				(0, 1 + len(lowerCAmelCase_							))							)
                      self.assertEqual(
                          encoding.offset_mapping[1]		,				(1 + len(lowerCAmelCase_							), 1 + len(lowerCAmelCase_							) + 1 + len(lowerCAmelCase_							))		,				)





import torch
from torch import nn



class 				a							(			nn.Module       ):







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_=1		,				lowerCAmelCase_=False							)  ->					Any:
        super().__init__()

        _A        =  n_token
        _A        =  d_embed
        _A        =  d_proj

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

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

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

        _A        =  nn.ModuleList()
        _A        =  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							)							):
                      _A   ,  _A        =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
                      _A        =  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							)							)

        _A        =  keep_order



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Optional[Any]:
        if proj is None:
               _A        =  nn.functional.linear(lowerCAmelCase_		,				lowerCAmelCase_		,				bias=lowerCAmelCase_							)
        else:
               # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
               _A        =  nn.functional.linear(lowerCAmelCase_		,				proj.t().contiguous()							)
               _A        =  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		,				lowerCAmelCase_		,				lowerCAmelCase_=None		,				lowerCAmelCase_=False							)  ->					List[Any]:

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

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

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

                      weights.append(lowerCAmelCase_							)
                      biases.append(lowerCAmelCase_							)

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

               _A        =  self._compute_logit(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)
               _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				dim=1							)

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

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

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

                             if indices_i.numel() == 0:
                                    continue

                             _A        =  labels.index_select(0		,				lowerCAmelCase_							) - l_idx
                             _A        =  head_logprob.index_select(0		,				lowerCAmelCase_							)
                             _A        =  hidden.index_select(0		,				lowerCAmelCase_							)
                      else:
                             _A        =  hidden

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

                             _A        =  self._compute_logit(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)
                             _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				dim=1							)
                             _A        =  self.cutoffs[0] + i - 1  # No probability for the head cluster
                             if labels is not None:
                                    _A        =  head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                                        1		,				target_i[:, None]							).squeeze(1							)
                             else:
                                    _A        =  head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
                                    _A        =  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		,				lowerCAmelCase_							)  ->					int:
        if self.n_clusters == 0:
               _A        =  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
               _A   ,  _A        =  [], []
               for i in range(len(self.cutoffs							)							):
                      if self.div_val == 1:
                             _A   ,  _A        =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
                             _A        =  self.out_layers[0].weight[l_idx:r_idx]
                             _A        =  self.out_layers[0].bias[l_idx:r_idx]
                      else:
                             _A        =  self.out_layers[i].weight
                             _A        =  self.out_layers[i].bias

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

                      weights.append(lowerCAmelCase_							)
                      biases.append(lowerCAmelCase_							)

               _A   ,  _A   ,  _A        =  weights[0], biases[0], self.out_projs[0]
               _A        =  self._compute_logit(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)

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

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

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

                             _A        =  self._compute_logit(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)
                             _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				dim=1							)

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

               return out




