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													=		False



class 						snake_case_ :






   def __init__(   self       :  Dict			,		lowercase_       :  Dict			,		lowercase_       :  Union[str, Any]=14			,		lowercase_       :  str=7			,		lowercase_       :  List[str]=True			,		lowercase_       :  Optional[Any]=False			,		lowercase_       :  Any=True			,		lowercase_       :  Tuple=True			,		lowercase_       :  List[Any]=True			,		lowercase_       :  List[str]=99			,		lowercase_       :  Any=32			,		lowercase_       :  Dict=5			,		lowercase_       :  List[Any]=37			,		lowercase_       :  Union[str, Any]="gelu"			,		lowercase_       :  int=0.1			,		lowercase_       :  Optional[int]=0.1			,		lowercase_       :  Union[str, Any]=5_12			,		lowercase_       :  Optional[int]=16			,		lowercase_       :  str=2			,		lowercase_       :  Any=3			,		lowercase_       :  str=4			,		lowercase_       :  Any=None			,		)      ->		Dict:
       lowercase__				:     Tuple					      =       parent
       lowercase__				:     Optional[Any]					      =       batch_size
       lowercase__				:     str					      =       seq_length
       lowercase__				:     Tuple					      =       is_training
       lowercase__				:     Optional[Any]					      =       use_token_type_ids
       lowercase__				:     Any					      =       use_input_mask
       lowercase__				:     str					      =       use_labels
       lowercase__				:     Tuple					      =       use_mc_token_ids
       lowercase__				:     Tuple					      =       vocab_size
       lowercase__				:     Tuple					      =       hidden_size
       lowercase__				:     int					      =       num_hidden_layers
       lowercase__				:     List[Any]					      =       intermediate_size
       lowercase__				:     str					      =       hidden_act
       lowercase__				:     List[str]					      =       hidden_dropout_prob
       lowercase__				:     List[Any]					      =       attention_probs_dropout_prob
       lowercase__				:     Any					      =       max_position_embeddings
       lowercase__				:     List[str]					      =       type_vocab_size
       lowercase__				:     Dict					      =       type_sequence_label_size
       lowercase__				:     List[str]					      =       num_labels
       lowercase__				:     Optional[int]					      =       num_choices
       lowercase__				:     Optional[int]					      =       scope
       lowercase__				:     Any					      =       vocab_size - 1
       lowercase__				:     Optional[int]					      =       vocab_size - 1
       lowercase__				:     int					      =       vocab_size - 1






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






   def    __UpperCamelCase  (   self       :  int			,		lowercase_       :  int=False			,		lowercase_       :  Union[str, Any]=False			,		lowercase_       :  List[Any]=False						)      ->		Optional[int]:
       lowercase__				:     str					      =       ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size						)

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

       lowercase__				:     List[str]					      =       None
       if self.use_token_type_ids:
           lowercase__				:     Optional[Any]					      =       ids_tensor([self.batch_size, self.seq_length]			,		self.type_vocab_size						)

       lowercase__				:     Dict					      =       None
       if self.use_mc_token_ids:
           lowercase__				:     List[Any]					      =       ids_tensor([self.batch_size, self.num_choices]			,		self.seq_length						)

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

       lowercase__				:     List[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    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  str=False			,		lowercase_       :  Optional[int]=False			,		lowercase_       :  List[Any]=False						)      ->		Union[str, 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    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		str:
       lowercase__				:     Optional[Any]					      =       self.get_config()
       lowercase__				:     Any					      =       3_00
       return config






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		int:
       (
           lowercase__     
       )				:     int					      =       self.prepare_config_and_inputs()

       lowercase__				:     Optional[Any]					      =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
       lowercase__				:     Optional[Any]					      =       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    __UpperCamelCase  (   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  List[str]			,		lowercase_       :  List[str]			,		lowercase_       :  str			,		lowercase_       :  str			,		*lowercase_       :  Dict						)      ->		List[str]:
       lowercase__				:     Tuple					      =       True
       lowercase__				:     List[Any]					      =       RwkvModel(config=lowercase_						)
       model.to(lowercase_						)
       model.eval()

       lowercase__				:     Optional[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    __UpperCamelCase  (   self       :  Optional[int]			,		lowercase_       :  List[str]			,		lowercase_       :  str			,		lowercase_       :  Any			,		lowercase_       :  int			,		lowercase_       :  Optional[Any]			,		*lowercase_       :  List[str]						)      ->		Union[str, Any]:
       lowercase__				:     List[Any]					      =       RwkvForCausalLM(lowercase_						)
       model.to(lowercase_						)
       model.eval()

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

       lowercase__				:     Union[str, Any]					      =       model(lowercase_						)
       lowercase__				:     Optional[int]					      =       outputs.last_hidden_state

       lowercase__				:     Any					      =       model(input_ids[:, :2]						)
       lowercase__				:     Optional[int]					      =       outputs.last_hidden_state

       # Using the state computed on the first inputs, we will get the same output
       lowercase__				:     Tuple					      =       model(input_ids[:, 2:]			,		state=outputs.state						)
       lowercase__				:     List[str]					      =       outputs.last_hidden_state

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






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

       lowercase__				:     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)						)
       result.loss.backward()







   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		Optional[Any]:
       lowercase__				:     Tuple					      =       self.prepare_config_and_inputs()

       (
           lowercase__     
       )				:     str					      =       config_and_inputs

       lowercase__				:     Optional[int]					      =       {"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 						snake_case_ (	__A						,__A						,__A						,unittest.TestCase ):
   __A			:	Tuple        =       (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
   __A			:	str        =       (
       {"feature-extraction": RwkvModel, "text-generation": RwkvForCausalLM} if is_torch_available() else {}
   )
   # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
   __A			:	List[Any]        =       False
   __A			:	List[str]        =       False
   __A			:	Union[str, Any]        =       False
   __A			:	int        =       False
   __A			:	Union[str, Any]        =       False  # Rwkv does not support head masking






   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Union[str, Any]:
       lowercase__				:     str					      =       RwkvModelTester(self						)
       lowercase__				:     Optional[int]					      =       ConfigTester(
           self			,		config_class=lowercase_			,		n_embd=37			,		common_properties=["hidden_size", "num_hidden_layers"]						)






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

       lowercase__				:     List[Any]					      =       container

       lowercase__				:     List[Any]					      =       (min_value >= expected_min) and (max_value <= expected_max)

       if not is_inside_interval:
           lowercase__				:     int					      =       "%s not found in %s" % (safe_repr(lowercase_						), safe_repr(lowercase_						))
           self.fail(self._formatMessage(lowercase_			,		lowercase_						)						)






   def    __UpperCamelCase  (   self       :  List[str]						)      ->		Tuple:
       self.config_tester.run_common_tests()






   def    __UpperCamelCase  (   self       :  Tuple						)      ->		Optional[Any]:
       lowercase__				:     str					      =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_rwkv_model(*lowercase_						)






   def    __UpperCamelCase  (   self       :  Any						)      ->		List[Any]:
       lowercase__				:     Optional[Any]					      =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_causl_lm(*lowercase_						)






   def    __UpperCamelCase  (   self       :  Any						)      ->		Optional[int]:
       lowercase__				:     Dict					      =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_state_equivalency(*lowercase_						)






   def    __UpperCamelCase  (   self       :  Dict						)      ->		Any:
       lowercase__				:     Tuple					      =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           lowercase__				:     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    __UpperCamelCase  (   self       :  List[str]						)      ->		Union[str, Any]:
       lowercase__				:     List[str]					      =       self.model_tester.prepare_config_and_inputs_for_common()
       lowercase__				:     Any					      =       True

       lowercase__				:     List[str]					      =       getattr(self.model_tester			,		"seq_length"			,		lowercase_						)

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

           lowercase__				:     List[Any]					      =       self._prepare_for_class(lowercase_			,		lowercase_						)
           lowercase__				:     int					      =       inputs["input_ids"].shape[0]
           with torch.no_grad():
               lowercase__				:     List[str]					      =       model(**lowercase_						)
           lowercase__				:     Optional[int]					      =       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"]
           lowercase__				:     List[Any]					      =       True
           lowercase__				:     Any					      =       model_class(lowercase_						)
           model.to(lowercase_						)
           model.eval()

           lowercase__				:     Any					      =       self._prepare_for_class(lowercase_			,		lowercase_						)
           lowercase__				:     List[str]					      =       inputs["input_ids"].shape[0]
           with torch.no_grad():
               lowercase__				:     List[Any]					      =       model(**lowercase_						)
           lowercase__				:     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]			,		)
           lowercase__				:     List[str]					      =       len(lowercase_						)

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

           lowercase__				:     str					      =       self._prepare_for_class(lowercase_			,		lowercase_						)
           lowercase__				:     int					      =       inputs["input_ids"].shape[0]
           with torch.no_grad():
               lowercase__				:     Union[str, Any]					      =       model(**lowercase_						)

           lowercase__				:     str					      =       1
           self.assertEqual(out_len + added_hidden_states			,		len(lowercase_						)						)

           lowercase__				:     Dict					      =       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    __UpperCamelCase  (   self       :  Tuple						)      ->		str:
       for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowercase__				:     Tuple					      =       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 						snake_case_ (	unittest.TestCase ):






   def    __UpperCamelCase  (   self       :  Tuple						)      ->		Tuple:
       lowercase__				:     Union[str, Any]					      =       "RWKV/rwkv-4-169m-pile"
       lowercase__				:     Tuple					      =       AutoTokenizer.from_pretrained(self.model_id						)






   def    __UpperCamelCase  (   self       :  str						)      ->		Any:
       lowercase__				:     int					      =       "Hello my name is Jasmine and I am a newbie to the"
       lowercase__				:     Dict					      =       RwkvForCausalLM.from_pretrained(self.model_id						).to(lowercase_						)

       lowercase__				:     Tuple					      =       self.tokenizer("Hello my name is"			,		return_tensors="pt"						).input_ids.to(lowercase_						)
       lowercase__				:     Optional[int]					      =       model.generate(lowercase_			,		max_new_tokens=10						)
       lowercase__				:     Any					      =       self.tokenizer.decode(output[0].tolist()						)

       self.assertEqual(lowercase_			,		lowercase_						)







   def    __UpperCamelCase  (   self       :  List[str]						)      ->		Union[str, Any]:
       lowercase__				:     List[str]					      =       "Hello my name is Jasmine and I am a newbie to the"

       lowercase__				:     List[str]					      =       self.tokenizer("Hello my name is"			,		return_tensors="pt"						).input_ids.to(lowercase_						)
       lowercase__				:     Optional[int]					      =       RwkvForCausalLM.from_pretrained(self.model_id			,		torch_dtype=torch.bfloataa						).to(lowercase_						)

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

       self.assertEqual(lowercase_			,		lowercase_						)




def     lowercase_       (    _lowerCamelCase							: str	,			_lowerCamelCase							: Optional[int]):
    if len(str(_lowerCamelCase)) == 1 or len(str(_lowerCamelCase)) == 1:
        return a * b

    lowercase__				:     Tuple					      =       max(len(str(_lowerCamelCase))	,			len(str(_lowerCamelCase)))
    lowercase__				:     Union[str, Any]					      =       ma // 2

    lowercase__      ,     lowercase__				:     Union[str, Any]					      =       divmod(_lowerCamelCase	,			10**ma)
    lowercase__      ,     lowercase__				:     Tuple					      =       divmod(_lowerCamelCase	,			10**ma)

    lowercase__				:     int					      =       karatsuba(_lowerCamelCase	,			_lowerCamelCase)
    lowercase__				:     Dict					      =       karatsuba((aa + aa)	,			(ba + ba))
    lowercase__				:     Optional[int]					      =       karatsuba(_lowerCamelCase	,			_lowerCamelCase)

    return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)

def     lowercase_       (    ):
    print(karatsuba(1_5463	,			2_3489))


if __name__ == "__main__":
      main()



