






"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


_UpperCamelCase :    Union[str, Any]      =			logging.get_logger(__name__)

_UpperCamelCase :    Any      =			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase :    int      =			{
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """allenai/longformer-base-4096""": (
            """https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
    },
}

_UpperCamelCase :    List[Any]      =			{
    """allenai/longformer-base-4096""": 40_96,
    """allenai/longformer-large-4096""": 40_96,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 40_96,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 40_96,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 40_96,
}






class __a ( snake_case_					):
  __lowercase     :     List[str]       						=       VOCAB_FILES_NAMES
  __lowercase     :     Any       						=       PRETRAINED_VOCAB_FILES_MAP
  __lowercase     :     str       						=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase     :     Tuple       						=       ['input_ids', 'attention_mask']
  __lowercase     :     List[str]       						=       LongformerTokenizer



  def __init__(self	:		Optional[Any]					,      lowercase_	:		Tuple=None					,      lowercase_	:		Any=None					,      lowercase_	:		Optional[int]=None					,      lowercase_	:		List[Any]="replace"					,      lowercase_	:		List[Any]="<s>"					,      lowercase_	:		Optional[Any]="</s>"					,      lowercase_	:		Dict="</s>"					,      lowercase_	:		Union[str, Any]="<s>"					,      lowercase_	:		List[Any]="<unk>"					,      lowercase_	:		Dict="<pad>"					,      lowercase_	:		List[str]="<mask>"					,      lowercase_	:		Union[str, Any]=False					,      lowercase_	:		Any=True					,      **lowercase_	:		List[Any]					,      )		->				Tuple:
   super().__init__(
       lowercase_					,      lowercase_					,      tokenizer_file=lowercase_					,      errors=lowercase_					,      bos_token=lowercase_					,      eos_token=lowercase_					,      sep_token=lowercase_					,      cls_token=lowercase_					,      unk_token=lowercase_					,      pad_token=lowercase_					,      mask_token=lowercase_					,      add_prefix_space=lowercase_					,      trim_offsets=lowercase_					,      **lowercase_					,      )

   __A						:							Union[str, Any]      =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
   if pre_tok_state.get('add_prefix_space'					,      lowercase_ ) != add_prefix_space:
    __A						:							Optional[Any]      =						getattr(lowercase_					,      pre_tok_state.pop('type' ) )
    __A						:							str      =						add_prefix_space
    __A						:							List[Any]      =						pre_tok_class(**lowercase_ )

   __A						:							List[str]      =						add_prefix_space

   __A						:							Optional[Any]      =						'post_processor'
   __A						:							Dict      =						getattr(self.backend_tokenizer					,      lowercase_					,      lowercase_ )
   if tokenizer_component_instance:
    __A						:							Union[str, Any]      =						json.loads(tokenizer_component_instance.__getstate__() )

    # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
    if "sep" in state:
     __A						:							Optional[Any]      =						tuple(state['sep'] )
    if "cls" in state:
     __A						:							Union[str, Any]      =						tuple(state['cls'] )

    __A						:							List[str]      =						False

    if state.get('add_prefix_space'					,      lowercase_ ) != add_prefix_space:
     __A						:							List[str]      =						add_prefix_space
     __A						:							Optional[Any]      =						True

    if state.get('trim_offsets'					,      lowercase_ ) != trim_offsets:
     __A						:							Dict      =						trim_offsets
     __A						:							List[Any]      =						True

    if changes_to_apply:
     __A						:							str      =						getattr(lowercase_					,      state.pop('type' ) )
     __A						:							Tuple      =						component_class(**lowercase_ )
     setattr(self.backend_tokenizer					,      lowercase_					,      lowercase_ )



  @property
  def     A_					(self	:		List[Any] )		->				str:
   if self._mask_token is None:
    if self.verbose:
     logger.error('Using mask_token, but it is not set yet.' )
    return None
   return str(self._mask_token )



  @mask_token.setter
  def     A_					(self	:		Optional[int]					,      lowercase_	:		int )		->				Any:
   __A						:							Optional[Any]      =						AddedToken(lowercase_					,      lstrip=lowercase_					,      rstrip=lowercase_ ) if isinstance(lowercase_					,      lowercase_ ) else value
   __A						:							str      =						value



  def     A_					(self	:		List[str]					,      *lowercase_	:		str					,      **lowercase_	:		Any )		->				BatchEncoding:
   __A						:							Dict      =						kwargs.get('is_split_into_words'					,      lowercase_ )
   assert self.add_prefix_space or not is_split_into_words, (
       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
       "to use it with pretokenized inputs."
   )

   return super()._batch_encode_plus(*lowercase_					,      **lowercase_ )



  def     A_					(self	:		Dict					,      *lowercase_	:		int					,      **lowercase_	:		Optional[Any] )		->				BatchEncoding:
   __A						:							Any      =						kwargs.get('is_split_into_words'					,      lowercase_ )

   assert self.add_prefix_space or not is_split_into_words, (
       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
       "to use it with pretokenized inputs."
   )

   return super()._encode_plus(*lowercase_					,      **lowercase_ )



  def     A_					(self	:		List[str]					,      lowercase_	:		str					,      lowercase_	:		Optional[str] = None )		->				Tuple[str]:
   __A						:							Dict      =						self._tokenizer.model.save(lowercase_					,      name=lowercase_ )
   return tuple(lowercase_ )



  def     A_					(self	:		List[Any]					,      lowercase_	:		Optional[Any]					,      lowercase_	:		Any=None )		->				int:
   __A						:							int      =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
   if token_ids_a is None:
    return output

   return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



  def     A_					(self	:		List[str]					,      lowercase_	:		List[int]					,      lowercase_	:		Optional[List[int]] = None )		->				List[int]:
   __A						:							Optional[Any]      =						[self.sep_token_id]
   __A						:							str      =						[self.cls_token_id]

   if token_ids_a is None:
    return len(cls + token_ids_a + sep ) * [0]
   return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]













"""simple docstring"""


import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
       RwkvForCausalLM,
       RwkvModel,
   )
   from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
   _UpperCamelCase :    Union[str, Any]      =			False






class __a :



  def __init__(self	:		str					,      lowercase_	:		Dict					,      lowercase_	:		str=14					,      lowercase_	:		List[str]=7					,      lowercase_	:		Any=True					,      lowercase_	:		List[str]=False					,      lowercase_	:		int=True					,      lowercase_	:		List[str]=True					,      lowercase_	:		str=True					,      lowercase_	:		Union[str, Any]=99					,      lowercase_	:		str=32					,      lowercase_	:		List[Any]=5					,      lowercase_	:		List[str]=37					,      lowercase_	:		str="gelu"					,      lowercase_	:		Any=0.1					,      lowercase_	:		Dict=0.1					,      lowercase_	:		List[Any]=512					,      lowercase_	:		Any=16					,      lowercase_	:		Union[str, Any]=2					,      lowercase_	:		Tuple=3					,      lowercase_	:		Optional[int]=4					,      lowercase_	:		int=None					,      )		->				Tuple:
   __A						:							Any      =						parent
   __A						:							int      =						batch_size
   __A						:							int      =						seq_length
   __A						:							int      =						is_training
   __A						:							int      =						use_token_type_ids
   __A						:							Union[str, Any]      =						use_input_mask
   __A						:							Any      =						use_labels
   __A						:							Union[str, Any]      =						use_mc_token_ids
   __A						:							List[Any]      =						vocab_size
   __A						:							List[str]      =						hidden_size
   __A						:							List[Any]      =						num_hidden_layers
   __A						:							Union[str, Any]      =						intermediate_size
   __A						:							Union[str, Any]      =						hidden_act
   __A						:							int      =						hidden_dropout_prob
   __A						:							Optional[Any]      =						attention_probs_dropout_prob
   __A						:							List[str]      =						max_position_embeddings
   __A						:							List[Any]      =						type_vocab_size
   __A						:							Tuple      =						type_sequence_label_size
   __A						:							Union[str, Any]      =						num_labels
   __A						:							Tuple      =						num_choices
   __A						:							int      =						scope
   __A						:							Union[str, Any]      =						vocab_size - 1
   __A						:							int      =						vocab_size - 1
   __A						:							Optional[Any]      =						vocab_size - 1



  def     A_					(self	:		List[str] )		->				Optional[int]:
   return RwkvConfig.from_pretrained('sgugger/rwkv-4-pile-7b' )



  def     A_					(self	:		Dict					,      lowercase_	:		str=False					,      lowercase_	:		Optional[int]=False					,      lowercase_	:		int=False )		->				List[str]:
   __A						:							List[Any]      =						ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size )

   __A						:							Optional[Any]      =						None
   if self.use_input_mask:
    __A						:							Tuple      =						random_attention_mask([self.batch_size, self.seq_length] )

   __A						:							Tuple      =						None
   if self.use_token_type_ids:
    __A						:							Optional[int]      =						ids_tensor([self.batch_size, self.seq_length]					,      self.type_vocab_size )

   __A						:							str      =						None
   if self.use_mc_token_ids:
    __A						:							int      =						ids_tensor([self.batch_size, self.num_choices]					,      self.seq_length )

   __A						:							List[Any]      =						None
   __A						:							int      =						None
   __A						:							Dict      =						None
   if self.use_labels:
    __A						:							List[str]      =						ids_tensor([self.batch_size]					,      self.type_sequence_label_size )
    __A						:							Dict      =						ids_tensor([self.batch_size, self.seq_length]					,      self.num_labels )
    __A						:							Tuple      =						ids_tensor([self.batch_size]					,      self.num_choices )

   __A						:							Any      =						self.get_config(
       gradient_checkpointing=lowercase_					,      scale_attn_by_inverse_layer_idx=lowercase_					,      reorder_and_upcast_attn=lowercase_					,      )

   return (
       config,
       input_ids,
       input_mask,
       None,
       token_type_ids,
       mc_token_ids,
       sequence_labels,
       token_labels,
       choice_labels,
   )



  def     A_					(self	:		Optional[int]					,      lowercase_	:		Optional[int]=False					,      lowercase_	:		Dict=False					,      lowercase_	:		List[str]=False )		->				Any:
   return RwkvConfig(
       vocab_size=self.vocab_size					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      intermediate_size=self.intermediate_size					,      activation_function=self.hidden_act					,      resid_pdrop=self.hidden_dropout_prob					,      attn_pdrop=self.attention_probs_dropout_prob					,      n_positions=self.max_position_embeddings					,      type_vocab_size=self.type_vocab_size					,      use_cache=lowercase_					,      bos_token_id=self.bos_token_id					,      eos_token_id=self.eos_token_id					,      pad_token_id=self.pad_token_id					,      gradient_checkpointing=lowercase_					,      scale_attn_by_inverse_layer_idx=lowercase_					,      reorder_and_upcast_attn=lowercase_					,      )



  def     A_					(self	:		Optional[int] )		->				Dict:
   __A						:							Any      =						self.get_config()
   __A						:							Any      =						300
   return config



  def     A_					(self	:		Tuple )		->				Dict:
   (
       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       
   )						:							Tuple      =						self.prepare_config_and_inputs()

   __A						:							Tuple      =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
   __A						:							List[str]      =						ids_tensor([self.batch_size, self.seq_length]					,      vocab_size=2 )

   return (
       config,
       input_ids,
       input_mask,
       head_mask,
       token_type_ids,
       sequence_labels,
       token_labels,
       choice_labels,
       encoder_hidden_states,
       encoder_attention_mask,
   )



  def     A_					(self	:		Any					,      lowercase_	:		Any					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		Optional[Any]					,      lowercase_	:		List[Any]					,      lowercase_	:		str					,      *lowercase_	:		Any )		->				Tuple:
   __A						:							List[str]      =						True
   __A						:							str      =						RwkvModel(config=lowercase_ )
   model.to(lowercase_ )
   model.eval()

   __A						:							List[Any]      =						model(lowercase_ )

   self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size) )
   self.parent.assertEqual(len(result.hidden_states )					,      config.num_hidden_layers + 1 )



  def     A_					(self	:		List[str]					,      lowercase_	:		Dict					,      lowercase_	:		List[str]					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		Optional[int]					,      lowercase_	:		List[Any]					,      *lowercase_	:		int )		->				Union[str, Any]:
   __A						:							Any      =						RwkvForCausalLM(lowercase_ )
   model.to(lowercase_ )
   model.eval()

   __A						:							Union[str, Any]      =						model(lowercase_					,      labels=lowercase_ )
   self.parent.assertEqual(result.loss.shape					,      () )
   self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size) )



  def     A_					(self	:		str					,      lowercase_	:		List[Any]					,      lowercase_	:		Dict					,      lowercase_	:		Tuple					,      lowercase_	:		str					,      lowercase_	:		Dict					,      *lowercase_	:		Optional[Any] )		->				Union[str, Any]:
   __A						:							Any      =						RwkvModel(config=lowercase_ )
   model.to(lowercase_ )
   model.eval()

   __A						:							Tuple      =						model(lowercase_ )
   __A						:							str      =						outputs.last_hidden_state

   __A						:							Optional[int]      =						model(input_ids[:, :2] )
   __A						:							Dict      =						outputs.last_hidden_state

   # Using the state computed on the first inputs, we will get the same output
   __A						:							Optional[int]      =						model(input_ids[:, 2:]					,      state=outputs.state )
   __A						:							Any      =						outputs.last_hidden_state

   self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]					,      dim=1 )					,      lowercase_					,      atol=1E-5 ) )



  def     A_					(self	:		List[str]					,      lowercase_	:		Optional[int]					,      lowercase_	:		List[str]					,      lowercase_	:		int					,      lowercase_	:		int					,      lowercase_	:		Union[str, Any]					,      *lowercase_	:		Dict					,      lowercase_	:		List[Any]=False )		->				str:
   __A						:							Tuple      =						RwkvForCausalLM(lowercase_ )
   model.to(lowercase_ )
   if gradient_checkpointing:
    model.gradient_checkpointing_enable()

   __A						:							Optional[int]      =						model(lowercase_					,      labels=lowercase_ )
   self.parent.assertEqual(result.loss.shape					,      () )
   self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size) )
   result.loss.backward()



  def     A_					(self	:		int )		->				Tuple:
   __A						:							Union[str, Any]      =						self.prepare_config_and_inputs()

   (
       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       
   )						:							int      =						config_and_inputs

   __A						:							Tuple      =						{'input_ids': input_ids}

   return config, inputs_dict






@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0			,reason='See https://github.com/huggingface/transformers/pull/24204'					)
@require_torch
class __a ( snake_case_			,snake_case_			,snake_case_			,unittest.TestCase					):
  __lowercase     :     Any       						=       (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
  __lowercase     :     Dict       						=       (
      {'feature-extraction': RwkvModel, 'text-generation': RwkvForCausalLM} if is_torch_available() else {}
  )
  # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
  __lowercase     :     List[str]       						=       False
  __lowercase     :     int       						=       False
  __lowercase     :     Optional[Any]       						=       False
  __lowercase     :     Union[str, Any]       						=       False
  __lowercase     :     Union[str, Any]       						=       False  # Rwkv does not support head masking



  def     A_					(self	:		List[str] )		->				Any:
   __A						:							List[str]      =						RwkvModelTester(self )
   __A						:							List[Any]      =						ConfigTester(
       self					,      config_class=lowercase_					,      n_embd=37					,      common_properties=['hidden_size', 'num_hidden_layers'] )



  def     A_					(self	:		int					,      lowercase_	:		Any					,      lowercase_	:		Optional[int]					,      lowercase_	:		Optional[Any]=None )		->				Tuple:
   if isinstance(lowercase_					,      torch.Tensor ):
    __A     ,       __A						:							str      =						member.max().item(), member.min().item()
   elif isinstance(lowercase_					,      lowercase_ ) or isinstance(lowercase_					,      lowercase_ ):
    __A     ,       __A						:							List[str]      =						max(lowercase_ ), min(lowercase_ )

   if not isinstance(lowercase_					,      lowercase_ ):
    raise TypeError('container should be a list or tuple' )
   elif len(lowercase_ ) != 2:
    raise ValueError('container should have 2 elements' )

   __A     ,       __A						:							Optional[Any]      =						container

   __A						:							List[str]      =						(min_value >= expected_min) and (max_value <= expected_max)

   if not is_inside_interval:
    __A						:							Optional[int]      =						'%s not found in %s' % (safe_repr(lowercase_ ), safe_repr(lowercase_ ))
    self.fail(self._formatMessage(lowercase_					,      lowercase_ ) )



  def     A_					(self	:		int )		->				Optional[Any]:
   self.config_tester.run_common_tests()



  def     A_					(self	:		Tuple )		->				str:
   __A						:							Dict      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_rwkv_model(*lowercase_ )



  def     A_					(self	:		List[Any] )		->				Union[str, Any]:
   __A						:							Any      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_causl_lm(*lowercase_ )



  def     A_					(self	:		Any )		->				Optional[int]:
   __A						:							Optional[int]      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_state_equivalency(*lowercase_ )



  def     A_					(self	:		List[str] )		->				int:
   __A     ,       __A						:							Optional[Any]      =						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __A						:							Optional[int]      =						model_class(config=lowercase_ )
    for name, param in model.named_parameters():
     if "time_decay" in name:
      if param.requires_grad:
       self.assertTrue(param.data.max().item() == 3.0 )
       self.assertTrue(param.data.min().item() == -5.0 )
     elif "time_first" in name:
      if param.requires_grad:
       # check if it's a ones like
       self.assertTrue(torch.allclose(param.data					,      torch.ones_like(param.data )					,      atol=1E-5					,      rtol=1E-5 ) )
     elif any(x in name for x in ['time_mix_key', 'time_mix_receptance'] ):
      if param.requires_grad:
       self.assertInterval(
           param.data					,      [0.0, 1.0]					,      msg=f"Parameter {name} of model {model_class} seems not properly initialized"					,      )
     elif "time_mix_value" in name:
      if param.requires_grad:
       self.assertInterval(
           param.data					,      [0.0, 1.3]					,      msg=f"Parameter {name} of model {model_class} seems not properly initialized"					,      )



  def     A_					(self	:		str )		->				Any:
   __A     ,       __A						:							List[str]      =						self.model_tester.prepare_config_and_inputs_for_common()
   __A						:							Optional[Any]      =						True

   __A						:							List[str]      =						getattr(self.model_tester					,      'seq_length'					,      lowercase_ )

   for model_class in self.all_model_classes:
    __A						:							str      =						True
    __A						:							str      =						False
    __A						:							List[str]      =						True
    __A						:							Optional[Any]      =						model_class(lowercase_ )
    model.to(lowercase_ )
    model.eval()

    __A						:							List[str]      =						self._prepare_for_class(lowercase_					,      lowercase_ )
    __A						:							Optional[int]      =						inputs['input_ids'].shape[0]
    with torch.no_grad():
     __A						:							Optional[Any]      =						model(**lowercase_ )
    __A						:							Union[str, Any]      =						outputs.attentions
    self.assertEqual(len(lowercase_ )					,      self.model_tester.num_hidden_layers )

    # check that output_attentions also work using config
    del inputs_dict["output_attentions"]
    __A						:							Optional[int]      =						True
    __A						:							Dict      =						model_class(lowercase_ )
    model.to(lowercase_ )
    model.eval()

    __A						:							Any      =						self._prepare_for_class(lowercase_					,      lowercase_ )
    __A						:							Any      =						inputs['input_ids'].shape[0]
    with torch.no_grad():
     __A						:							List[Any]      =						model(**lowercase_ )
    __A						:							Optional[Any]      =						outputs.attentions
    self.assertEqual(len(lowercase_ )					,      self.model_tester.num_hidden_layers )

    self.assertListEqual(
        list(attentions[0].shape[-3:] )					,      [batch_size, seq_len, config.hidden_size]					,      )
    __A						:							Tuple      =						len(lowercase_ )

    # Check attention is always last and order is fine
    __A						:							Any      =						True
    __A						:							str      =						True
    __A						:							List[str]      =						model_class(lowercase_ )
    model.to(lowercase_ )
    model.eval()

    __A						:							List[Any]      =						self._prepare_for_class(lowercase_					,      lowercase_ )
    __A						:							Union[str, Any]      =						inputs['input_ids'].shape[0]
    with torch.no_grad():
     __A						:							Dict      =						model(**lowercase_ )

    __A						:							Dict      =						1
    self.assertEqual(out_len + added_hidden_states					,      len(lowercase_ ) )

    __A						:							Optional[Any]      =						outputs.attentions

    self.assertEqual(len(lowercase_ )					,      self.model_tester.num_hidden_layers )
    self.assertListEqual(
        list(self_attentions[0].shape[-3:] )					,      [batch_size, seq_len, config.hidden_size]					,      )



  @slow
  def     A_					(self	:		Optional[Any] )		->				Any:
   for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __A						:							Optional[int]      =						RwkvModel.from_pretrained(lowercase_ )
    self.assertIsNotNone(lowercase_ )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0			,reason='See https://github.com/huggingface/transformers/pull/24204'					)
@slow
class __a ( unittest.TestCase					):



  def     A_					(self	:		str )		->				Optional[Any]:
   __A						:							Optional[int]      =						'RWKV/rwkv-4-169m-pile'
   __A						:							Tuple      =						AutoTokenizer.from_pretrained(self.model_id )



  def     A_					(self	:		Optional[Any] )		->				str:
   __A						:							int      =						'Hello my name is Jasmine and I am a newbie to the'
   __A						:							str      =						RwkvForCausalLM.from_pretrained(self.model_id ).to(lowercase_ )

   __A						:							Union[str, Any]      =						self.tokenizer('Hello my name is'					,      return_tensors='pt' ).input_ids.to(lowercase_ )
   __A						:							List[str]      =						model.generate(lowercase_					,      max_new_tokens=10 )
   __A						:							List[str]      =						self.tokenizer.decode(output[0].tolist() )

   self.assertEqual(lowercase_					,      lowercase_ )



  def     A_					(self	:		Any )		->				Optional[Any]:
   __A						:							Optional[int]      =						'Hello my name is Jasmine and I am a newbie to the'

   __A						:							Any      =						self.tokenizer('Hello my name is'					,      return_tensors='pt' ).input_ids.to(lowercase_ )
   __A						:							int      =						RwkvForCausalLM.from_pretrained(self.model_id					,      torch_dtype=torch.bfloataa ).to(lowercase_ )

   __A						:							str      =						model.generate(lowercase_					,      max_new_tokens=10 )
   __A						:							Union[str, Any]      =						self.tokenizer.decode(output[0].tolist() )

   self.assertEqual(lowercase_					,      lowercase_ )





