



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:
 __snake_case		:Union[str, Any] 						=	False

class        _A			:



       def __init__(						self				:	str	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	str=14	,						__SCREAMING_SNAKE_CASE				:	List[str]=7	,						__SCREAMING_SNAKE_CASE				:	Any=True	,						__SCREAMING_SNAKE_CASE				:	List[str]=False	,						__SCREAMING_SNAKE_CASE				:	int=True	,						__SCREAMING_SNAKE_CASE				:	List[str]=True	,						__SCREAMING_SNAKE_CASE				:	str=True	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]=99	,						__SCREAMING_SNAKE_CASE				:	str=32	,						__SCREAMING_SNAKE_CASE				:	List[Any]=5	,						__SCREAMING_SNAKE_CASE				:	List[str]=37	,						__SCREAMING_SNAKE_CASE				:	str="gelu"	,						__SCREAMING_SNAKE_CASE				:	Any=0.1	,						__SCREAMING_SNAKE_CASE				:	Dict=0.1	,						__SCREAMING_SNAKE_CASE				:	List[Any]=512	,						__SCREAMING_SNAKE_CASE				:	Any=16	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]=2	,						__SCREAMING_SNAKE_CASE				:	Tuple=3	,						__SCREAMING_SNAKE_CASE				:	Optional[int]=4	,						__SCREAMING_SNAKE_CASE				:	int=None	,						):


           '''simple docstring'''

           __a					=							parent
           __a					=							batch_size
           __a					=							seq_length
           __a					=							is_training
           __a					=							use_token_type_ids
           __a					=							use_input_mask
           __a					=							use_labels
           __a					=							use_mc_token_ids
           __a					=							vocab_size
           __a					=							hidden_size
           __a					=							num_hidden_layers
           __a					=							intermediate_size
           __a					=							hidden_act
           __a					=							hidden_dropout_prob
           __a					=							attention_probs_dropout_prob
           __a					=							max_position_embeddings
           __a					=							type_vocab_size
           __a					=							type_sequence_label_size
           __a					=							num_labels
           __a					=							num_choices
           __a					=							scope
           __a					=							vocab_size - 1
           __a					=							vocab_size - 1
           __a					=							vocab_size - 1



       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           return RwkvConfig.from_pretrained('''sgugger/rwkv-4-pile-7b''')



       def 						_lowerCamelCase     (						self				:	Dict	,						__SCREAMING_SNAKE_CASE				:	str=False	,						__SCREAMING_SNAKE_CASE				:	Optional[int]=False	,						__SCREAMING_SNAKE_CASE				:	int=False):


           '''simple docstring'''

           __a					=							ids_tensor([self.batch_size, self.seq_length]	,						self.vocab_size)

           __a					=							None
           if self.use_input_mask:
               __a					=							random_attention_mask([self.batch_size, self.seq_length])

           __a					=							None
           if self.use_token_type_ids:
               __a					=							ids_tensor([self.batch_size, self.seq_length]	,						self.type_vocab_size)

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

           __a					=							None
           __a					=							None
           __a					=							None
           if self.use_labels:
               __a					=							ids_tensor([self.batch_size]	,						self.type_sequence_label_size)
               __a					=							ids_tensor([self.batch_size, self.seq_length]	,						self.num_labels)
               __a					=							ids_tensor([self.batch_size]	,						self.num_choices)

           __a					=							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 						_lowerCamelCase     (						self				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Optional[int]=False	,						__SCREAMING_SNAKE_CASE				:	Dict=False	,						__SCREAMING_SNAKE_CASE				:	List[str]=False):


           '''simple docstring'''

           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 						_lowerCamelCase     (						self				:	Optional[int]):


           '''simple docstring'''

           __a					=							self.get_config()
           __a					=							300
           return config



       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

           (
               __a       
           )					=							self.prepare_config_and_inputs()

           __a					=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
           __a					=							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 						_lowerCamelCase     (						self				:	Any	,						__SCREAMING_SNAKE_CASE				:	Any	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	str	,						*__SCREAMING_SNAKE_CASE				:	Any):


           '''simple docstring'''

           __a					=							True
           __a					=							RwkvModel(config=lowercase_)
           model.to(lowercase_)
           model.eval()

           __a					=							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 						_lowerCamelCase     (						self				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	List[Any]	,						*__SCREAMING_SNAKE_CASE				:	int):


           '''simple docstring'''

           __a					=							RwkvForCausalLM(lowercase_)
           model.to(lowercase_)
           model.eval()

           __a					=							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 						_lowerCamelCase     (						self				:	str	,						__SCREAMING_SNAKE_CASE				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	Dict	,						*__SCREAMING_SNAKE_CASE				:	Optional[Any]):


           '''simple docstring'''

           __a					=							RwkvModel(config=lowercase_)
           model.to(lowercase_)
           model.eval()

           __a					=							model(lowercase_)
           __a					=							outputs.last_hidden_state

           __a					=							model(input_ids[:, :2])
           __a					=							outputs.last_hidden_state

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

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



       def 						_lowerCamelCase     (						self				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	int	,						__SCREAMING_SNAKE_CASE				:	int	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]	,						*__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	List[Any]=False):


           '''simple docstring'''

           __a					=							RwkvForCausalLM(lowercase_)
           model.to(lowercase_)
           if gradient_checkpointing:
               model.gradient_checkpointing_enable()

           __a					=							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 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           __a					=							self.prepare_config_and_inputs()

           (
               __a       
           )					=							config_and_inputs

           __a					=							{'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       ):
       UpperCamelCase__    :       Any		   =		(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
       UpperCamelCase__    :       Dict		   =		(
           {'feature-extraction': RwkvModel, 'text-generation': RwkvForCausalLM} if is_torch_available() else {}
       )
       # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
       UpperCamelCase__    :       List[str]		   =		False
       UpperCamelCase__    :       int		   =		False
       UpperCamelCase__    :       Optional[Any]		   =		False
       UpperCamelCase__    :       Union[str, Any]		   =		False
       UpperCamelCase__    :       Union[str, Any]		   =		False  # Rwkv does not support head masking



       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           __a					=							RwkvModelTester(self)
           __a					=							ConfigTester(
               self	,						config_class=lowercase_	,						n_embd=37	,						common_properties=['''hidden_size''', '''num_hidden_layers'''])



       def 						_lowerCamelCase     (						self				:	int	,						__SCREAMING_SNAKE_CASE				:	Any	,						__SCREAMING_SNAKE_CASE				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]=None):


           '''simple docstring'''

           if isinstance(lowercase_	,						torch.Tensor):
               __a					=							member.max().item(), member.min().item()
           elif isinstance(lowercase_	,						lowercase_) or isinstance(lowercase_	,						lowercase_):
               __a					=							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					=							container

           __a					=							(min_value >= expected_min) and (max_value <= expected_max)

           if not is_inside_interval:
               __a					=							'%s not found in %s' % (safe_repr(lowercase_), safe_repr(lowercase_))
               self.fail(self._formatMessage(lowercase_	,						lowercase_))



       def 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           self.config_tester.run_common_tests()



       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

           __a					=							self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_rwkv_model(*lowercase_)



       def 						_lowerCamelCase     (						self				:	List[Any]):


           '''simple docstring'''

           __a					=							self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_causl_lm(*lowercase_)



       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

           __a					=							self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_state_equivalency(*lowercase_)



       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           __a					=							self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __a					=							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 						_lowerCamelCase     (						self				:	str):


           '''simple docstring'''

           __a					=							self.model_tester.prepare_config_and_inputs_for_common()
           __a					=							True

           __a					=							getattr(self.model_tester	,						'''seq_length'''	,						lowercase_)

           for model_class in self.all_model_classes:
               __a					=							True
               __a					=							False
               __a					=							True
               __a					=							model_class(lowercase_)
               model.to(lowercase_)
               model.eval()

               __a					=							self._prepare_for_class(lowercase_	,						lowercase_)
               __a					=							inputs['input_ids'].shape[0]
               with torch.no_grad():
                   __a					=							model(**lowercase_)
               __a					=							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					=							True
               __a					=							model_class(lowercase_)
               model.to(lowercase_)
               model.eval()

               __a					=							self._prepare_for_class(lowercase_	,						lowercase_)
               __a					=							inputs['input_ids'].shape[0]
               with torch.no_grad():
                   __a					=							model(**lowercase_)
               __a					=							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					=							len(lowercase_)

               # Check attention is always last and order is fine
               __a					=							True
               __a					=							True
               __a					=							model_class(lowercase_)
               model.to(lowercase_)
               model.eval()

               __a					=							self._prepare_for_class(lowercase_	,						lowercase_)
               __a					=							inputs['input_ids'].shape[0]
               with torch.no_grad():
                   __a					=							model(**lowercase_)

               __a					=							1
               self.assertEqual(out_len + added_hidden_states	,						len(lowercase_))

               __a					=							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 						_lowerCamelCase     (						self				:	Optional[Any]):


           '''simple docstring'''

           for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a					=							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 						_lowerCamelCase     (						self				:	str):


           '''simple docstring'''

           __a					=							'RWKV/rwkv-4-169m-pile'
           __a					=							AutoTokenizer.from_pretrained(self.model_id)



       def 						_lowerCamelCase     (						self				:	Optional[Any]):


           '''simple docstring'''

           __a					=							'Hello my name is Jasmine and I am a newbie to the'
           __a					=							RwkvForCausalLM.from_pretrained(self.model_id).to(lowercase_)

           __a					=							self.tokenizer('''Hello my name is'''	,						return_tensors='''pt''').input_ids.to(lowercase_)
           __a					=							model.generate(lowercase_	,						max_new_tokens=10)
           __a					=							self.tokenizer.decode(output[0].tolist())

           self.assertEqual(lowercase_	,						lowercase_)





       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

           __a					=							'Hello my name is Jasmine and I am a newbie to the'

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

           __a					=							model.generate(lowercase_	,						max_new_tokens=10)
           __a					=							self.tokenizer.decode(output[0].tolist())

           self.assertEqual(lowercase_	,						lowercase_)













"""simple docstring"""


def 					a			(  UpperCamelCase_				:					float		)	->		int:
 return int(UpperCamelCase_		) if x - int(UpperCamelCase_		) <= 0 else int(UpperCamelCase_		) + 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()





