


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:
 __lowerCAmelCase									=				False





class     __a     :
 def __init__(  self							,					lowerCAmelCase__							,					lowerCAmelCase__=14							,					lowerCAmelCase__=7							,					lowerCAmelCase__=True							,					lowerCAmelCase__=False							,					lowerCAmelCase__=True							,					lowerCAmelCase__=True							,					lowerCAmelCase__=True							,					lowerCAmelCase__=99							,					lowerCAmelCase__=32							,					lowerCAmelCase__=5							,					lowerCAmelCase__=37							,					lowerCAmelCase__="gelu"							,					lowerCAmelCase__=0.1							,					lowerCAmelCase__=0.1							,					lowerCAmelCase__=512							,					lowerCAmelCase__=16							,					lowerCAmelCase__=2							,					lowerCAmelCase__=3							,					lowerCAmelCase__=4							,					lowerCAmelCase__=None							,					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  List[Any]         =  parent
        lowercase__:  Tuple         =  batch_size
        lowercase__:  Optional[int]         =  seq_length
        lowercase__:  Optional[int]         =  is_training
        lowercase__:  Any         =  use_token_type_ids
        lowercase__:  Optional[int]         =  use_input_mask
        lowercase__:  List[Any]         =  use_labels
        lowercase__:  int         =  use_mc_token_ids
        lowercase__:  List[str]         =  vocab_size
        lowercase__:  Union[str, Any]         =  hidden_size
        lowercase__:  str         =  num_hidden_layers
        lowercase__:  Optional[int]         =  intermediate_size
        lowercase__:  List[Any]         =  hidden_act
        lowercase__:  List[str]         =  hidden_dropout_prob
        lowercase__:  Tuple         =  attention_probs_dropout_prob
        lowercase__:  Optional[int]         =  max_position_embeddings
        lowercase__:  List[str]         =  type_vocab_size
        lowercase__:  Optional[Any]         =  type_sequence_label_size
        lowercase__:  List[Any]         =  num_labels
        lowercase__:  Tuple         =  num_choices
        lowercase__:  int         =  scope
        lowercase__:  Optional[int]         =  vocab_size - 1
        lowercase__:  str         =  vocab_size - 1
        lowercase__:  int         =  vocab_size - 1
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Union[str, Any]:




        '''simple docstring'''
        return RwkvConfig.from_pretrained('sgugger/rwkv-4-pile-7b'					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__=False							,					lowerCAmelCase__=False							,					lowerCAmelCase__=False					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  List[Any]         =  ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size					)

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

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

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

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

        lowercase__:  List[str]         =  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 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__=False							,					lowerCAmelCase__=False							,					lowerCAmelCase__=False					)   ->							List[str]:




        '''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 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Any:




        '''simple docstring'''
        lowercase__:  str         =  self.get_config()
        lowercase__:  Optional[Any]         =  300
        return config
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        (
            (
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,			
        ):  Union[str, Any]         =  self.prepare_config_and_inputs()

        lowercase__:  Any         =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
        lowercase__:  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 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					*lowerCAmelCase__					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  True
        lowercase__:  List[Any]         =  RwkvModel(config=__lowercase					)
        model.to(__lowercase					)
        model.eval()

        lowercase__:  List[str]         =  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 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					*lowerCAmelCase__					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Dict         =  RwkvForCausalLM(__lowercase					)
        model.to(__lowercase					)
        model.eval()

        lowercase__:  List[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 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					*lowerCAmelCase__					)   ->							Any:




        '''simple docstring'''
        lowercase__:  List[Any]         =  RwkvModel(config=__lowercase					)
        model.to(__lowercase					)
        model.eval()

        lowercase__:  Tuple         =  model(__lowercase					)
        lowercase__:  Union[str, Any]         =  outputs.last_hidden_state

        lowercase__:  List[Any]         =  model(input_ids[:, :2]					)
        lowercase__:  List[str]         =  outputs.last_hidden_state

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

        self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]							,					dim=1					)							,					__lowercase							,					atol=1E-5					)					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					*lowerCAmelCase__							,					lowerCAmelCase__=False					)   ->							Dict:




        '''simple docstring'''
        lowercase__:  Any         =  RwkvForCausalLM(__lowercase					)
        model.to(__lowercase					)
        if gradient_checkpointing:
               model.gradient_checkpointing_enable()

        lowercase__:  Optional[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 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  int         =  self.prepare_config_and_inputs()

        (
            (
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,		(
            lowercase__	
        )     ,			
        ):  List[str]         =  config_and_inputs

        lowercase__:  Dict         =  {'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     (       __UpperCamelCase			,			__UpperCamelCase			,			__UpperCamelCase			,			unittest.TestCase ):
 __lowercase					:				Optional[Any]  	=					(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
 __lowercase					:				Tuple  	=					(
     {'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					:				Tuple  	=					False
 __lowercase					:				Any  	=					False
 __lowercase					:				Dict  	=					False
 __lowercase					:				str  	=					False  # Rwkv does not support head masking
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Any         =  RwkvModelTester(self					)
        lowercase__:  Optional[int]         =  ConfigTester(
            self							,					config_class=__lowercase							,					n_embd=37							,					common_properties=['hidden_size', 'num_hidden_layers']					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__=None					)   ->							Optional[Any]:




        '''simple docstring'''
        if isinstance(__lowercase							,					torch.Tensor					):
               lowercase__     ,		lowercase__:  Optional[int]         =  member.max().item(), member.min().item()
        elif isinstance(__lowercase							,					__lowercase					) or isinstance(__lowercase							,					__lowercase					):
               lowercase__     ,		lowercase__:  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__     ,		lowercase__:  Tuple         =  container

        lowercase__:  Tuple         =  (min_value >= expected_min) and (max_value <= expected_max)

        if not is_inside_interval:
               lowercase__:  List[str]         =  '%s not found in %s' % (safe_repr(__lowercase					), safe_repr(__lowercase					))
               self.fail(self._formatMessage(__lowercase							,					__lowercase					)					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Union[str, Any]:




        '''simple docstring'''
        self.config_tester.run_common_tests()
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  List[Any]         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_rwkv_model(*__lowercase					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  Dict         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_causl_lm(*__lowercase					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__:  Optional[int]         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_state_equivalency(*__lowercase					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__     ,		lowercase__:  Union[str, Any]         =  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 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Any:




        '''simple docstring'''
        lowercase__     ,		lowercase__:  Optional[int]         =  self.model_tester.prepare_config_and_inputs_for_common()
        lowercase__:  List[Any]         =  True

        lowercase__:  Optional[Any]         =  getattr(self.model_tester							,					'seq_length'							,					__lowercase					)

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

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

               lowercase__:  Optional[int]         =  self._prepare_for_class(__lowercase							,					__lowercase					)
               lowercase__:  List[Any]         =  inputs['input_ids'].shape[0]
               with torch.no_grad():
                      lowercase__:  Dict         =  model(**__lowercase					)
               lowercase__:  str         =  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__:  int         =  len(__lowercase					)

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

               lowercase__:  Tuple         =  self._prepare_for_class(__lowercase							,					__lowercase					)
               lowercase__:  Optional[int]         =  inputs['input_ids'].shape[0]
               with torch.no_grad():
                      lowercase__:  Optional[int]         =  model(**__lowercase					)

               lowercase__:  Any         =  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 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__:  List[Any]         =  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 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Any:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  'RWKV/rwkv-4-169m-pile'
        lowercase__:  Dict         =  AutoTokenizer.from_pretrained(self.model_id					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  int         =  'Hello my name is Jasmine and I am a newbie to the'
        lowercase__:  Union[str, Any]         =  RwkvForCausalLM.from_pretrained(self.model_id					).to(__lowercase					)

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

        self.assertEqual(__lowercase							,					__lowercase					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  'Hello my name is Jasmine and I am a newbie to the'

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

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

        self.assertEqual(__lowercase							,					__lowercase					)










"""simple docstring"""



from math import exp




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->float:
 return 8 * x - 2 * exp(-x			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 UpperCAmelCase__ 						=				lower_bound
 UpperCAmelCase__ 						=				upper_bound
 for _ in range(0       ,	_SCREAMING_SNAKE_CASE			):
  UpperCAmelCase__						,	UpperCAmelCase__ 						=				xa, xa - (f(_SCREAMING_SNAKE_CASE			) * (xa - xa)) / (f(_SCREAMING_SNAKE_CASE			) - f(_SCREAMING_SNAKE_CASE			))
 return xa


if __name__ == "__main__":
 print(F'''Example: {secant_method(1, 3, 2)}''')



