



"""simple docstring"""






import torch
from torch import nn






class 	__snake_case	(     nn.Module):




  def __init__(			self					:						List[str]						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						Tuple						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						str=1						, __lowerCAmelCase					:						Union[str, Any]=False		):






         """simple docstring"""






         super().__init__()

         _lowerCamelCase						:    List[str]         =			n_token
         _lowerCamelCase						:    List[Any]         =			d_embed
         _lowerCamelCase						:    str         =			d_proj

         _lowerCamelCase						:    Optional[Any]         =			cutoffs + [n_token]
         _lowerCamelCase						:    Tuple         =			[0] + self.cutoffs
         _lowerCamelCase						:    int         =			div_val

         _lowerCamelCase						:    Any         =			self.cutoffs[0]
         _lowerCamelCase						:    List[str]         =			len(self.cutoffs		) - 1
         _lowerCamelCase						:    Dict         =			self.shortlist_size + self.n_clusters

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

         _lowerCamelCase						:    Union[str, Any]         =			nn.ModuleList()
         _lowerCamelCase						:    str         =			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		)		):
                       _lowerCamelCase						:    List[str]         =			self.cutoff_ends[i], self.cutoff_ends[i + 1]
                       _lowerCamelCase						:    List[Any]         =			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		)		)

         _lowerCamelCase						:    int         =			keep_order




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						Union[str, Any]						, __lowerCAmelCase					:						int		):






         """simple docstring"""






         if proj is None:
                _lowerCamelCase						:    List[str]         =			nn.functional.linear(_A						, _A						, bias=_A		)
         else:
                # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
                _lowerCamelCase						:    List[str]         =			nn.functional.linear(_A						, proj.t().contiguous()		)
                _lowerCamelCase						:    int         =			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 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						Tuple=None						, __lowerCAmelCase					:						Optional[Any]=False		):






         """simple docstring"""






         if labels is not None:
                # Shift so that tokens < n predict n
                _lowerCamelCase						:    int         =			hidden[..., :-1, :].contiguous()
                _lowerCamelCase						:    Union[str, Any]         =			labels[..., 1:].contiguous()
                _lowerCamelCase						:    Tuple         =			hidden.view(-1						, hidden.size(-1		)		)
                _lowerCamelCase						:    Optional[int]         =			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:
                _lowerCamelCase						:    Union[str, Any]         =			hidden.view(-1						, hidden.size(-1		)		)

         if self.n_clusters == 0:
                _lowerCamelCase						:    Optional[Any]         =			self._compute_logit(_A						, self.out_layers[0].weight						, self.out_layers[0].bias						, self.out_projs[0]		)
                if labels is not None:
                       _lowerCamelCase						:    Any         =			labels != -1_0_0
                       _lowerCamelCase						:    Any         =			torch.zeros_like(_A						, dtype=hidden.dtype						, device=hidden.device		)
                       _lowerCamelCase						:    str         =			(
                           -nn.functional.log_softmax(_A						, dim=-1		)[mask].gather(1						, labels[mask].unsqueeze(1		)		).squeeze(1		)
                       )
                else:
                       _lowerCamelCase						:    Optional[Any]         =			nn.functional.log_softmax(_A						, dim=-1		)
         else:
                # construct weights and biases
                _lowerCamelCase						:    Any         =			[], []
                for i in range(len(self.cutoffs		)		):
                       if self.div_val == 1:
                              _lowerCamelCase						:    str         =			self.cutoff_ends[i], self.cutoff_ends[i + 1]
                              _lowerCamelCase						:    str         =			self.out_layers[0].weight[l_idx:r_idx]
                              _lowerCamelCase						:    List[Any]         =			self.out_layers[0].bias[l_idx:r_idx]
                       else:
                              _lowerCamelCase						:    int         =			self.out_layers[i].weight
                              _lowerCamelCase						:    List[Any]         =			self.out_layers[i].bias

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

                       weights.append(_A		)
                       biases.append(_A		)

                _lowerCamelCase						:    Tuple         =			weights[0], biases[0], self.out_projs[0]

                _lowerCamelCase						:    Optional[int]         =			self._compute_logit(_A						, _A						, _A						, _A		)
                _lowerCamelCase						:    Tuple         =			nn.functional.log_softmax(_A						, dim=1		)

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

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

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

                              if indices_i.numel() == 0:
                                     continue

                              _lowerCamelCase						:    List[Any]         =			labels.index_select(0						, _A		) - l_idx
                              _lowerCamelCase						:    Optional[Any]         =			head_logprob.index_select(0						, _A		)
                              _lowerCamelCase						:    int         =			hidden.index_select(0						, _A		)
                       else:
                              _lowerCamelCase						:    List[Any]         =			hidden

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

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






         """simple docstring"""






         if self.n_clusters == 0:
                _lowerCamelCase						:    Dict         =			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
                _lowerCamelCase						:    Any         =			[], []
                for i in range(len(self.cutoffs		)		):
                       if self.div_val == 1:
                              _lowerCamelCase						:    Any         =			self.cutoff_ends[i], self.cutoff_ends[i + 1]
                              _lowerCamelCase						:    str         =			self.out_layers[0].weight[l_idx:r_idx]
                              _lowerCamelCase						:    int         =			self.out_layers[0].bias[l_idx:r_idx]
                       else:
                              _lowerCamelCase						:    Any         =			self.out_layers[i].weight
                              _lowerCamelCase						:    List[Any]         =			self.out_layers[i].bias

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

                       weights.append(_A		)
                       biases.append(_A		)

                _lowerCamelCase						:    Union[str, Any]         =			weights[0], biases[0], self.out_projs[0]
                _lowerCamelCase						:    Optional[Any]         =			self._compute_logit(_A						, _A						, _A						, _A		)

                _lowerCamelCase						:    Union[str, Any]         =			hidden.new_empty((head_logit.size(0		), self.n_token)		)
                _lowerCamelCase						:    int         =			nn.functional.log_softmax(_A						, dim=1		)

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

                       if i == 0:
                              _lowerCamelCase						:    List[Any]         =			head_logprob[:, : self.cutoffs[0]]
                       else:
                              _lowerCamelCase						:    Dict         =			weights[i], biases[i], self.out_projs[i]

                              _lowerCamelCase						:    Optional[int]         =			self._compute_logit(_A						, _A						, _A						, _A		)
                              _lowerCamelCase						:    Optional[Any]         =			nn.functional.log_softmax(_A						, dim=1		)

                              _lowerCamelCase						:    List[str]         =			head_logprob[:, -i] + tail_logprob_i
                              _lowerCamelCase						:    str         =			logprob_i

                return out







"""simple docstring"""






import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class 	__snake_case	(     _lowercase  ,					unittest.TestCase):
  snake_case__    :       Optional[int]        =						BlenderbotSmallTokenizer
  snake_case__    :       List[str]        =						False




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         super().setUp()

         _lowerCamelCase						:    str         =			['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
         _lowerCamelCase						:    Any         =			dict(zip(__lowerCAmelCase						, range(len(__lowerCAmelCase		)		)		)		)

         _lowerCamelCase						:    Any         =			['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
         _lowerCamelCase						:    List[str]         =			{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

         _lowerCamelCase						:    Dict         =			os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['''vocab_file''']		)
         _lowerCamelCase						:    List[str]         =			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 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, **__lowerCAmelCase					:						List[str]		):






         """simple docstring"""






         kwargs.update(self.special_tokens_map		)
         return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname						, **__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			'''adapt act apte'''
         _lowerCamelCase						:    Tuple         =			'''adapt act apte'''
         return input_text, output_text




  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			BlenderbotSmallTokenizer(self.vocab_file						, self.merges_file						, **self.special_tokens_map		)
         _lowerCamelCase						:    int         =			'''adapt act apte'''
         _lowerCamelCase						:    Optional[Any]         =			['''adapt''', '''act''', '''ap@@''', '''te''']
         _lowerCamelCase						:    Optional[Any]         =			tokenizer.tokenize(__lowerCAmelCase		)
         self.assertListEqual(__lowerCAmelCase						, __lowerCAmelCase		)

         _lowerCamelCase						:    List[Any]         =			[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

         _lowerCamelCase						:    Dict         =			[0, 1, 2, 3, 4, 5]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase		)						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''		)
         assert tok('''sam'''		).input_ids == [1_3_8_4]
         _lowerCamelCase						:    List[str]         =			'''I am a small frog.'''
         _lowerCamelCase						:    str         =			tok([src_text]						, padding=__lowerCAmelCase						, truncation=__lowerCAmelCase		)['''input_ids''']
         _lowerCamelCase						:    Any         =			tok.batch_decode(__lowerCAmelCase						, skip_special_tokens=__lowerCAmelCase						, clean_up_tokenization_spaces=__lowerCAmelCase		)[0]
         assert src_text != decoded  # I wish it did!
         assert decoded == "i am a small frog ."




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''		)
         _lowerCamelCase						:    Optional[Any]         =			'''I am a small frog .'''
         _lowerCamelCase						:    str         =			'''.'''
         _lowerCamelCase						:    str         =			tok(__lowerCAmelCase		)['''input_ids''']
         _lowerCamelCase						:    Dict         =			tok(__lowerCAmelCase		)['''input_ids''']

         assert encoded[-1] == encoded_dot[0]


