
import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase			:     Any           =   get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')




@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase_			( _A  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		GPTSwaTokenizer
  a__           =		False
  a__           =		True
  a__           =		False
  def 							_lowercase							( self			: Any    )			->	List[str]:







    """simple docstring"""






    super().setUp()

    # We have a SentencePiece fixture for testing
    __magic_name__				     =       GPTSwaTokenizer(UpperCamelCase__			,							eos_token="""<unk>"""			,							bos_token="""<unk>"""			,							pad_token="""<unk>"""    )

    tokenizer.save_pretrained(self.tmpdirname    )
  def 							_lowercase							( self			: int			,							UpperCamelCase__			: List[str]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       """This is a test"""
    __magic_name__				     =       """This is a test"""
    return input_text, output_text
  def 							_lowercase							( self			: Optional[int]    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       """<s>"""
    __magic_name__				     =       1

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__    )			,							UpperCamelCase__    )
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__    )			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Optional[Any]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       list(self.get_tokenizer().get_vocab().keys()    )

    self.assertEqual(vocab_keys[0]			,							"""<unk>"""    )
    self.assertEqual(vocab_keys[1]			,							"""<s>"""    )
    self.assertEqual(vocab_keys[-1]			,							"""j"""    )
    self.assertEqual(len(UpperCamelCase__    )			,							2000    )
  def 							_lowercase							( self			: Dict    )			->	Any:







    """simple docstring"""






    self.assertEqual(self.get_tokenizer().vocab_size			,							2000    )
  def 							_lowercase							( self			: Any    )			->	str:







    """simple docstring"""






    __magic_name__				     =       GPTSwaTokenizer(UpperCamelCase__    )

    __magic_name__				     =       tokenizer.tokenize("""This is a test"""    )
    self.assertListEqual(UpperCamelCase__			,							["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]    )

    self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__    )			,							[465, 287, 265, 631, 842]    )

    __magic_name__				     =       tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
    # fmt: off
    self.assertListEqual(
        UpperCamelCase__			,							["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]			,							)
    # fmt: on

    __magic_name__				     =       tokenizer.convert_tokens_to_ids(UpperCamelCase__    )
    self.assertListEqual(
        UpperCamelCase__			,							[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]			,							)

    __magic_name__				     =       tokenizer.convert_ids_to_tokens(UpperCamelCase__    )
    # fmt: off
    self.assertListEqual(
        UpperCamelCase__			,							["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]    )
    # fmt: on
  def 							_lowercase							( self			: Optional[int]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       GPTSwaTokenizer(UpperCamelCase__    )
    __magic_name__				     =       ["""This is a test""", """I was born in 92000, and this is falsé."""]
    __magic_name__				     =       [
        [465, 287, 265, 631, 842],
        [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
    ]

    # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
    for text, expected_ids in zip(UpperCamelCase__			,							UpperCamelCase__    ):
      self.assertListEqual(tokenizer.encode_fast(UpperCamelCase__    )			,							UpperCamelCase__    )

    # Test that decode_fast returns the input text
    for text, token_ids in zip(UpperCamelCase__			,							UpperCamelCase__    ):
      self.assertEqual(tokenizer.decode_fast(UpperCamelCase__    )			,							UpperCamelCase__    )





  @slow
  def 							_lowercase							( self			: Dict    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       [
        """<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')""",
        """Hey there, how are you doing this fine day?""",
        """This is a text with a trailing spaces followed by a dot     .""",
        """Häj sväjs lillebrör! =)""",
        """Det är inget fel på Mr. Cool""",
    ]

    # fmt: off
    __magic_name__				     =       {"""input_ids""": [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
    # fmt: on
    self.tokenizer_integration_test_util(
        expected_encoding=UpperCamelCase__			,							model_name="""AI-Sweden/gpt-sw3-126m"""			,							sequences=UpperCamelCase__			,							)


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




