


import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin



class 							_UpperCamelCase (						A,A						):

   '''simple docstring'''







   @register_to_config
   def __init__(  self  : Union[str, Any]       ,  _lowerCamelCase  : int = 1_2_8       ,  _lowerCamelCase  : int = 2_5_6       ,  _lowerCamelCase  : float = 2_000.0       ,  _lowerCamelCase  : int = 7_6_8       ,  _lowerCamelCase  : int = 1_2       ,  _lowerCamelCase  : int = 1_2       ,  _lowerCamelCase  : int = 6_4       ,  _lowerCamelCase  : int = 2_0_4_8       ,  _lowerCamelCase  : float = 0.1       ,  ):






         '''simple docstring'''



         super().__init__()

         __lowerCamelCase     :      int       						=						nn.Sequential(
             nn.Linear(_lowerCamelCase       ,  d_model * 4       ,  bias=_lowerCamelCase							)       ,  nn.SiLU()       ,  nn.Linear(d_model * 4       ,  d_model * 4       ,  bias=_lowerCamelCase							)       ,  nn.SiLU()       ,  )

         __lowerCamelCase     :      Optional[int]       						=						nn.Embedding(_lowerCamelCase       ,  _lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						False

         __lowerCamelCase     :      Tuple       						=						nn.Linear(_lowerCamelCase       ,  _lowerCamelCase       ,  bias=_lowerCamelCase							)

         __lowerCamelCase     :      Optional[Any]       						=						nn.Dropout(p=_lowerCamelCase							)

         __lowerCamelCase     :      int       						=						nn.ModuleList()
         for lyr_num in range(_lowerCamelCase							):
               # FiLM conditional T5 decoder
               __lowerCamelCase     :      Optional[Any]       						=						DecoderLayer(d_model=_lowerCamelCase       ,  d_kv=_lowerCamelCase       ,  num_heads=_lowerCamelCase       ,  d_ff=_lowerCamelCase       ,  dropout_rate=_lowerCamelCase							)
               self.decoders.append(_lowerCamelCase							)

         __lowerCamelCase     :      int       						=						TaLayerNorm(_lowerCamelCase							)

         __lowerCamelCase     :      Any       						=						nn.Dropout(p=_lowerCamelCase							)
         __lowerCamelCase     :      int       						=						nn.Linear(_lowerCamelCase       ,  _lowerCamelCase       ,  bias=_lowerCamelCase							)

   def   _snake_case    (  self  : int       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						torch.mul(query_input.unsqueeze(-1							)       ,  key_input.unsqueeze(-2							)							)
         return mask.unsqueeze(-3							)


   def   _snake_case    (  self  : Union[str, Any]       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Dict							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						decoder_input_tokens.shape
         assert decoder_noise_time.shape == (batch,)

         # decoder_noise_time is in [0, 1), so rescale to expected timing range.
         __lowerCamelCase     :      int       						=						get_timestep_embedding(
             decoder_noise_time * self.config.max_decoder_noise_time       ,  embedding_dim=self.config.d_model       ,  max_period=self.config.max_decoder_noise_time       ,  ).to(dtype=self.dtype							)

         __lowerCamelCase     :      Optional[int]       						=						self.conditioning_emb(_lowerCamelCase							).unsqueeze(1							)

         assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

         __lowerCamelCase     :      Optional[int]       						=						decoder_input_tokens.shape[1]

         # If we want to use relative positions for audio context, we can just offset
         # this sequence by the length of encodings_and_masks.
         __lowerCamelCase     :      List[str]       						=						torch.broadcast_to(
             torch.arange(_lowerCamelCase       ,  device=decoder_input_tokens.device							)       ,  (batch, seq_length)       ,  )

         __lowerCamelCase     :      Union[str, Any]       						=						self.position_encoding(_lowerCamelCase							)

         __lowerCamelCase     :      Dict       						=						self.continuous_inputs_projection(_lowerCamelCase							)
         inputs += position_encodings
         __lowerCamelCase     :      str       						=						self.dropout(_lowerCamelCase							)

         # decoder: No padding present.
         __lowerCamelCase     :      Any       						=						torch.ones(
             decoder_input_tokens.shape[:2]       ,  device=decoder_input_tokens.device       ,  dtype=inputs.dtype							)

         # Translate encoding masks to encoder-decoder masks.
         __lowerCamelCase     :      Union[str, Any]       						=						[(x, self.encoder_decoder_mask(_lowerCamelCase       ,  _lowerCamelCase							)) for x, y in encodings_and_masks]

         # cross attend style: concat encodings
         __lowerCamelCase     :      Optional[int]       						=						torch.cat([x[0] for x in encodings_and_encdec_masks]       ,  dim=1							)
         __lowerCamelCase     :      Dict       						=						torch.cat([x[1] for x in encodings_and_encdec_masks]       ,  dim=-1							)

         for lyr in self.decoders:
               __lowerCamelCase     :      List[str]       						=						lyr(
                   _lowerCamelCase       ,  conditioning_emb=_lowerCamelCase       ,  encoder_hidden_states=_lowerCamelCase       ,  encoder_attention_mask=_lowerCamelCase       ,  )[0]

         __lowerCamelCase     :      Optional[int]       						=						self.decoder_norm(_lowerCamelCase							)
         __lowerCamelCase     :      List[str]       						=						self.post_dropout(_lowerCamelCase							)

         __lowerCamelCase     :      List[Any]       						=						self.spec_out(_lowerCamelCase							)
         return spec_out



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def __init__(  self  : List[str]       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Any=1E-6							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      List[str]       						=						nn.ModuleList()

         # cond self attention: layer 0
         self.layer.append(
             TaLayerSelfAttentionCond(d_model=_lowerCamelCase       ,  d_kv=_lowerCamelCase       ,  num_heads=_lowerCamelCase       ,  dropout_rate=_lowerCamelCase							)							)

         # cross attention: layer 1
         self.layer.append(
             TaLayerCrossAttention(
                 d_model=_lowerCamelCase       ,  d_kv=_lowerCamelCase       ,  num_heads=_lowerCamelCase       ,  dropout_rate=_lowerCamelCase       ,  layer_norm_epsilon=_lowerCamelCase       ,  )							)

         # Film Cond MLP + dropout: last layer
         self.layer.append(
             TaLayerFFCond(d_model=_lowerCamelCase       ,  d_ff=_lowerCamelCase       ,  dropout_rate=_lowerCamelCase       ,  layer_norm_epsilon=_lowerCamelCase							)							)


   def   _snake_case    (  self  : Dict       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Union[str, Any]=None       ,  _lowerCamelCase  : Dict=None       ,  _lowerCamelCase  : Dict=None       ,  _lowerCamelCase  : Optional[Any]=None       ,  _lowerCamelCase  : Optional[int]=None       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						self.layer[0](
             _lowerCamelCase       ,  conditioning_emb=_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  )

         if encoder_hidden_states is not None:
               __lowerCamelCase     :      Tuple       						=						torch.where(encoder_attention_mask > 0       ,  0       ,  -1E10							).to(
                   encoder_hidden_states.dtype							)

               __lowerCamelCase     :      List[Any]       						=						self.layer[1](
                   _lowerCamelCase       ,  key_value_states=_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  )

         # Apply Film Conditional Feed Forward layer
         __lowerCamelCase     :      List[Any]       						=						self.layer[-1](_lowerCamelCase       ,  _lowerCamelCase							)

         return (hidden_states,)



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def __init__(  self  : Tuple       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : List[str]							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      Union[str, Any]       						=						TaLayerNorm(_lowerCamelCase							)
         __lowerCamelCase     :      List[str]       						=						TaFiLMLayer(in_features=d_model * 4       ,  out_features=_lowerCamelCase							)
         __lowerCamelCase     :      Optional[int]       						=						Attention(query_dim=_lowerCamelCase       ,  heads=_lowerCamelCase       ,  dim_head=_lowerCamelCase       ,  out_bias=_lowerCamelCase       ,  scale_qk=_lowerCamelCase							)
         __lowerCamelCase     :      int       						=						nn.Dropout(_lowerCamelCase							)


   def   _snake_case    (  self  : int       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Union[str, Any]=None       ,  _lowerCamelCase  : Optional[int]=None       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						self.layer_norm(_lowerCamelCase							)

         if conditioning_emb is not None:
               __lowerCamelCase     :      int       						=						self.FiLMLayer(_lowerCamelCase       ,  _lowerCamelCase							)

         # Self-attention block
         __lowerCamelCase     :      int       						=						self.attention(_lowerCamelCase							)

         __lowerCamelCase     :      int       						=						hidden_states + self.dropout(_lowerCamelCase							)

         return hidden_states



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def __init__(  self  : Any       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Any							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      str       						=						Attention(query_dim=_lowerCamelCase       ,  heads=_lowerCamelCase       ,  dim_head=_lowerCamelCase       ,  out_bias=_lowerCamelCase       ,  scale_qk=_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						TaLayerNorm(_lowerCamelCase       ,  eps=_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						nn.Dropout(_lowerCamelCase							)


   def   _snake_case    (  self  : str       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : str=None       ,  _lowerCamelCase  : Optional[int]=None       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						self.layer_norm(_lowerCamelCase							)
         __lowerCamelCase     :      List[Any]       						=						self.attention(
             _lowerCamelCase       ,  encoder_hidden_states=_lowerCamelCase       ,  attention_mask=attention_mask.squeeze(1							)       ,  )
         __lowerCamelCase     :      Union[str, Any]       						=						hidden_states + self.dropout(_lowerCamelCase							)
         return layer_output



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def __init__(  self  : Tuple       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : List[str]							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      List[str]       						=						TaDenseGatedActDense(d_model=_lowerCamelCase       ,  d_ff=_lowerCamelCase       ,  dropout_rate=_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						TaFiLMLayer(in_features=d_model * 4       ,  out_features=_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						TaLayerNorm(_lowerCamelCase       ,  eps=_lowerCamelCase							)
         __lowerCamelCase     :      Any       						=						nn.Dropout(_lowerCamelCase							)


   def   _snake_case    (  self  : List[str]       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Union[str, Any]=None							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						self.layer_norm(_lowerCamelCase							)
         if conditioning_emb is not None:
               __lowerCamelCase     :      str       						=						self.film(_lowerCamelCase       ,  _lowerCamelCase							)

         __lowerCamelCase     :      int       						=						self.DenseReluDense(_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						hidden_states + self.dropout(_lowerCamelCase							)
         return hidden_states



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def __init__(  self  : Tuple       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : Union[str, Any]							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      int       						=						nn.Linear(_lowerCamelCase       ,  _lowerCamelCase       ,  bias=_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						nn.Linear(_lowerCamelCase       ,  _lowerCamelCase       ,  bias=_lowerCamelCase							)
         __lowerCamelCase     :      int       						=						nn.Linear(_lowerCamelCase       ,  _lowerCamelCase       ,  bias=_lowerCamelCase							)
         __lowerCamelCase     :      Optional[int]       						=						nn.Dropout(_lowerCamelCase							)
         __lowerCamelCase     :      Dict       						=						NewGELUActivation()


   def   _snake_case    (  self  : List[str]       ,  _lowerCamelCase  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						self.act(self.wi_a(_lowerCamelCase							)							)
         __lowerCamelCase     :      List[str]       						=						self.wi_a(_lowerCamelCase							)
         __lowerCamelCase     :      Optional[Any]       						=						hidden_gelu * hidden_linear
         __lowerCamelCase     :      str       						=						self.dropout(_lowerCamelCase							)

         __lowerCamelCase     :      str       						=						self.wo(_lowerCamelCase							)
         return hidden_states



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def __init__(  self  : Dict       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : Tuple=1E-6							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      int       						=						nn.Parameter(torch.ones(_lowerCamelCase							)							)
         __lowerCamelCase     :      Any       						=						eps


   def   _snake_case    (  self  : Union[str, Any]       ,  _lowerCamelCase  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						hidden_states.to(torch.floataa							).pow(2							).mean(-1       ,  keepdim=_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						hidden_states * torch.rsqrt(variance + self.variance_epsilon							)

         # convert into half-precision if necessary
         if self.weight.dtype in [torch.floataa, torch.bfloataa]:
               __lowerCamelCase     :      Dict       						=						hidden_states.to(self.weight.dtype							)

         return self.weight * hidden_states



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def   _snake_case    (  self  : Optional[Any]       ,  _lowerCamelCase  : torch.Tensor							):






         '''simple docstring'''



         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi							) * (input + 0.044_715 * torch.pow(_lowerCamelCase       ,  3.0							))							))



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def __init__(  self  : Any       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : str							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      List[str]       						=						nn.Linear(_lowerCamelCase       ,  out_features * 2       ,  bias=_lowerCamelCase							)


   def   _snake_case    (  self  : List[str]       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						self.scale_bias(_lowerCamelCase							)
         __lowerCamelCase						, __lowerCamelCase     :      List[Any]       						=						torch.chunk(_lowerCamelCase       ,  2       ,  -1							)
         __lowerCamelCase     :      Tuple       						=						x * (1 + scale) + shift
         return x







import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCamelCase    :						str     =							get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
@require_tokenizers
class 							_UpperCamelCase (						A,unittest.TestCase						):

   '''simple docstring'''







   a_				:							List[Any]    = ReformerTokenizer
   a_				:							Optional[int]    = ReformerTokenizerFast
   a_				:							Optional[int]    = True
   a_				:							List[Any]    = False
   a_				:							Any    = True

   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         super().setUp()

         __lowerCamelCase     :      str       						=						ReformerTokenizer(_lowerCamelCase       ,  keep_accents=_lowerCamelCase							)
         tokenizer.save_pretrained(self.tmpdirname							)

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						"""<s>"""
         __lowerCamelCase     :      Any       						=						1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase							)       ,  _lowerCamelCase							)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase							)       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						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(_lowerCamelCase							)       ,  1_0_0_0							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         self.assertEqual(self.get_tokenizer().vocab_size       ,  1_0_0_0							)

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         if not self.test_rust_tokenizer:
               return

         __lowerCamelCase     :      Dict       						=						self.get_tokenizer()
         __lowerCamelCase     :      Optional[int]       						=						self.get_rust_tokenizer()

         __lowerCamelCase     :      Optional[Any]       						=						"""I was born in 92000, and this is falsé."""

         __lowerCamelCase     :      Union[str, Any]       						=						tokenizer.tokenize(_lowerCamelCase							)
         __lowerCamelCase     :      Optional[int]       						=						rust_tokenizer.tokenize(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase       ,  _lowerCamelCase							)

         __lowerCamelCase     :      str       						=						tokenizer.encode(_lowerCamelCase       ,  add_special_tokens=_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						rust_tokenizer.encode(_lowerCamelCase       ,  add_special_tokens=_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase       ,  _lowerCamelCase							)

         __lowerCamelCase     :      str       						=						self.get_rust_tokenizer()
         __lowerCamelCase     :      Dict       						=						tokenizer.encode(_lowerCamelCase							)
         __lowerCamelCase     :      Dict       						=						rust_tokenizer.encode(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : int=1_5							):






         '''simple docstring'''



         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
                     __lowerCamelCase     :      Union[str, Any]       						=						self.rust_tokenizer_class.from_pretrained(_lowerCamelCase       ,  **_lowerCamelCase							)

                     # Simple input
                     __lowerCamelCase     :      Any       						=						"""This is a simple input"""
                     __lowerCamelCase     :      Optional[Any]       						=						["""This is a simple input 1""", """This is a simple input 2"""]
                     __lowerCamelCase     :      Union[str, Any]       						=						("""This is a simple input""", """This is a pair""")
                     __lowerCamelCase     :      Optional[int]       						=						[
                         ("""This is a simple input 1""", """This is a simple input 2"""),
                         ("""This is a simple pair 1""", """This is a simple pair 2"""),
                     ]

                     # Simple input tests
                     self.assertRaises(_lowerCamelCase       ,  tokenizer_r.encode       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""							)

                     # Simple input
                     self.assertRaises(_lowerCamelCase       ,  tokenizer_r.encode_plus       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""							)

                     # Simple input
                     self.assertRaises(
                         _lowerCamelCase       ,  tokenizer_r.batch_encode_plus       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""       ,  )

                     # Pair input
                     self.assertRaises(_lowerCamelCase       ,  tokenizer_r.encode       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""							)

                     # Pair input
                     self.assertRaises(_lowerCamelCase       ,  tokenizer_r.encode_plus       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""							)

                     # Pair input
                     self.assertRaises(
                         _lowerCamelCase       ,  tokenizer_r.batch_encode_plus       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""       ,  )

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         pass

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						ReformerTokenizer(_lowerCamelCase       ,  keep_accents=_lowerCamelCase							)

         __lowerCamelCase     :      List[Any]       						=						tokenizer.tokenize("""This is a test"""							)
         self.assertListEqual(_lowerCamelCase       ,  ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(_lowerCamelCase							)       ,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]       ,  )

         __lowerCamelCase     :      List[Any]       						=						tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
         self.assertListEqual(
             _lowerCamelCase       ,  [
                 SPIECE_UNDERLINE + """I""",
                 SPIECE_UNDERLINE + """was""",
                 SPIECE_UNDERLINE + """b""",
                 """or""",
                 """n""",
                 SPIECE_UNDERLINE + """in""",
                 SPIECE_UNDERLINE + """""",
                 """9""",
                 """2""",
                 """0""",
                 """0""",
                 """0""",
                 """,""",
                 SPIECE_UNDERLINE + """and""",
                 SPIECE_UNDERLINE + """this""",
                 SPIECE_UNDERLINE + """is""",
                 SPIECE_UNDERLINE + """f""",
                 """al""",
                 """s""",
                 """é""",
                 """.""",
             ]       ,  )
         __lowerCamelCase     :      Union[str, Any]       						=						tokenizer.convert_tokens_to_ids(_lowerCamelCase							)
         self.assertListEqual(
             _lowerCamelCase       ,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]       ,  )

         __lowerCamelCase     :      List[str]       						=						tokenizer.convert_ids_to_tokens(_lowerCamelCase							)
         self.assertListEqual(
             _lowerCamelCase       ,  [
                 SPIECE_UNDERLINE + """I""",
                 SPIECE_UNDERLINE + """was""",
                 SPIECE_UNDERLINE + """b""",
                 """or""",
                 """n""",
                 SPIECE_UNDERLINE + """in""",
                 SPIECE_UNDERLINE + """""",
                 """<unk>""",
                 """2""",
                 """0""",
                 """0""",
                 """0""",
                 """,""",
                 SPIECE_UNDERLINE + """and""",
                 SPIECE_UNDERLINE + """this""",
                 SPIECE_UNDERLINE + """is""",
                 SPIECE_UNDERLINE + """f""",
                 """al""",
                 """s""",
                 """<unk>""",
                 """.""",
             ]       ,  )

   @cached_property
   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""							)

   @slow
   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						"""Hello World!"""
         __lowerCamelCase     :      Any       						=						[1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

         self.assertListEqual(_lowerCamelCase       ,  self.big_tokenizer.encode(_lowerCamelCase							)							)

   @slow
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						(
             """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
             """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
         )
         __lowerCamelCase     :      List[str]       						=						[
             1_0_8,
             2_6_5,
             2_4,
             1_1_1,
             4,
             2_5_8,
             1_5_6,
             3_5,
             2_8,
             2_7_5,
             3,
             2_5_9,
             2_9_7,
             2_6_0,
             8_4,
             4,
             3_5,
             1_1_0,
             4_4,
             8,
             2_5_9,
             9_1,
             2_6_8,
             2_1,
             1_1,
             2_0_9,
             2_7_4,
             1_0_9,
             2_6_6,
             2_7_7,
             1_1_7,
             8_6,
             9_3,
             3_1_5,
             2_5_8,
             2_7_8,
             2_5_8,
             2_7_7,
             2_5_8,
             0,
             2_5_8,
             2_8_8,
             2_5_8,
             3_1_9,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             2_8_7,
             2_5_8,
             3_1_5,
             2_5_8,
             2_8_9,
             2_5_8,
             2_7_8,
             9_9,
             2_6_9,
             2_6_6,
             2_6_2,
             8,
             2_5_9,
             2_4_1,
             4,
             2_1_7,
             2_3_0,
             2_6_8,
             2_6_6,
             5_5,
             1_6_8,
             1_0_6,
             7_5,
             1_9_3,
             2_6_6,
             2_2_3,
             2_7,
             4_9,
             2_6,
             2_8_2,
             2_5,
             2_6_4,
             2_9_9,
             1_9,
             2_6,
             0,
             2_5_8,
             2_7_7,
             1_1_7,
             8_6,
             9_3,
             1_7_6,
             1_8_3,
             2_7_0,
             1_1,
             2_6_2,
             4_2,
             6_1,
             2_6_5,
         ]

         self.assertListEqual(_lowerCamelCase       ,  self.big_tokenizer.encode(_lowerCamelCase							)							)

   @require_torch
   @slow
   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         import torch

         from transformers import ReformerConfig, ReformerModel

         # Build sequence
         __lowerCamelCase     :      int       						=						list(self.big_tokenizer.get_vocab().keys()							)[:1_0]
         __lowerCamelCase     :      Union[str, Any]       						=						""" """.join(_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						self.big_tokenizer.encode_plus(_lowerCamelCase       ,  return_tensors="""pt"""							)
         __lowerCamelCase     :      Union[str, Any]       						=						self.big_tokenizer.batch_encode_plus([sequence, sequence]       ,  return_tensors="""pt"""							)

         __lowerCamelCase     :      List[Any]       						=						ReformerConfig()
         # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
         __lowerCamelCase     :      List[str]       						=						encoded_sequence["""input_ids"""].shape
         __lowerCamelCase     :      Optional[Any]       						=						ReformerModel(_lowerCamelCase							)

         # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

         with torch.no_grad():
               model(**_lowerCamelCase							)
               model(**_lowerCamelCase							)


   @slow
   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						{"""input_ids""": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], """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, 1, 1, 1, 1, 1]]}  # noqa: E501
         # fmt: on

         # This tokenizer does not know some characters like ")".
         # That is the reason why we use very simple texts here.
         # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
         __lowerCamelCase     :      int       						=						[
             """This is a very simple sentence.""",
             """The quick brown fox jumps over the lazy dog.""",
         ]

         self.tokenizer_integration_test_util(
             expected_encoding=_lowerCamelCase       ,  model_name="""google/reformer-crime-and-punishment"""       ,  revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""       ,  padding=_lowerCamelCase       ,  sequences=_lowerCamelCase       ,  )



