






"""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:
       lowercase_								=	False


class    snake_case						:


   '''simple docstring'''

   def __init__(      self					:	Tuple,			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	str=14,			_lowerCamelCase					:	str=7,			_lowerCamelCase					:	Dict=True,			_lowerCamelCase					:	str=False,			_lowerCamelCase					:	Optional[Any]=True,			_lowerCamelCase					:	Tuple=True,			_lowerCamelCase					:	List[Any]=True,			_lowerCamelCase					:	List[Any]=99,			_lowerCamelCase					:	Dict=32,			_lowerCamelCase					:	Tuple=5,			_lowerCamelCase					:	Any=37,			_lowerCamelCase					:	Union[str, Any]="gelu",			_lowerCamelCase					:	List[str]=0.1,			_lowerCamelCase					:	List[str]=0.1,			_lowerCamelCase					:	str=5_12,			_lowerCamelCase					:	Optional[int]=16,			_lowerCamelCase					:	Tuple=2,			_lowerCamelCase					:	int=3,			_lowerCamelCase					:	Optional[Any]=4,			_lowerCamelCase					:	Optional[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 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any],			_lowerCamelCase					:	Dict=False,			_lowerCamelCase					:	Union[str, Any]=False,			_lowerCamelCase					:	List[str]=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=__snake_case,			scale_attn_by_inverse_layer_idx=__snake_case,			reorder_and_upcast_attn=__snake_case,			)

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int,			_lowerCamelCase					:	int=False,			_lowerCamelCase					:	Union[str, Any]=False,			_lowerCamelCase					:	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=__snake_case,			bos_token_id=self.bos_token_id,			eos_token_id=self.eos_token_id,			pad_token_id=self.pad_token_id,			gradient_checkpointing=__snake_case,			scale_attn_by_inverse_layer_idx=__snake_case,			reorder_and_upcast_attn=__snake_case,			)


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					self.get_config()
     __A		     =					3_00
     return config


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''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 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	Any,			_lowerCamelCase					:	str,			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	Dict,			*_lowerCamelCase					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					True
     __A		     =					RwkvModel(config=__snake_case    )
     model.to(__snake_case    )
     model.eval()

     __A		     =					model(__snake_case    )

     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					:	List[str],			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	str,			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	List[str],			*_lowerCamelCase					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					RwkvForCausalLM(__snake_case    )
     model.to(__snake_case    )
     model.eval()

     __A		     =					model(__snake_case,			labels=__snake_case    )
     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					:	List[str],			_lowerCamelCase					:	Optional[int],			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	int,			_lowerCamelCase					:	List[Any],			*_lowerCamelCase					:	Dict    ):

     '''simple docstring'''
     __A		     =					RwkvModel(config=__snake_case    )
     model.to(__snake_case    )
     model.eval()

     __A		     =					model(__snake_case    )
     __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    ),			__snake_case,			atol=1e-5    )    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	Any,			_lowerCamelCase					:	str,			_lowerCamelCase					:	List[str],			*_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	Dict=False    ):

     '''simple docstring'''
     __A		     =					RwkvForCausalLM(__snake_case    )
     model.to(__snake_case    )
     if gradient_checkpointing:
       model.gradient_checkpointing_enable()

     __A		     =					model(__snake_case,			labels=__snake_case    )
     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					:	Dict    ):

     '''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    snake_case						(				_lowercase  ,      _lowercase  ,      _lowercase  ,      unittest.TestCase		):


   '''simple docstring'''

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					RwkvModelTester(self    )
     __A		     =					ConfigTester(
         self,			config_class=__snake_case,			n_embd=37,			common_properties=['''hidden_size''', '''num_hidden_layers''']    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	List[Any]=None    ):

     '''simple docstring'''
     if isinstance(__snake_case,			torch.Tensor    ):
       __A		     =					member.max().item(), member.min().item()
     elif isinstance(__snake_case,			__snake_case    ) or isinstance(__snake_case,			__snake_case    ):
       __A		     =					max(__snake_case    ), min(__snake_case    )

     if not isinstance(__snake_case,			__snake_case    ):
       raise TypeError('''container should be a list or tuple'''    )
     elif len(__snake_case    ) != 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(__snake_case    ), safe_repr(__snake_case    ))
       self.fail(self._formatMessage(__snake_case,			__snake_case    )    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     self.config_tester.run_common_tests()


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

     '''simple docstring'''
     __A		     =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_rwkv_model(*__snake_case    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_causl_lm(*__snake_case    )


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

     '''simple docstring'''
     __A		     =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_state_equivalency(*__snake_case    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       __A		     =					model_class(config=__snake_case    )
       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					:	Dict    ):

     '''simple docstring'''
     __A		     =					self.model_tester.prepare_config_and_inputs_for_common()
     __A		     =					True

     __A		     =					getattr(self.model_tester,			'''seq_length''',			__snake_case    )

     for model_class in self.all_model_classes:
       __A		     =					True
       __A		     =					False
       __A		     =					True
       __A		     =					model_class(__snake_case    )
       model.to(__snake_case    )
       model.eval()

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

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       __A		     =					True
       __A		     =					model_class(__snake_case    )
       model.to(__snake_case    )
       model.eval()

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

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

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

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

       __A		     =					1
       self.assertEqual(out_len + added_hidden_states,			len(__snake_case    )    )

       __A		     =					outputs.attentions

       self.assertEqual(len(__snake_case    ),			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					:	List[str]    ):

     '''simple docstring'''
     for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __A		     =					RwkvModel.from_pretrained(__snake_case    )
       self.assertIsNotNone(__snake_case    )





@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		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					'Hello my name is Jasmine and I am a newbie to the'
     __A		     =					RwkvForCausalLM.from_pretrained(self.model_id    ).to(__snake_case    )

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

     self.assertEqual(__snake_case,			__snake_case    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''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(__snake_case    )
     __A		     =					RwkvForCausalLM.from_pretrained(self.model_id,			torch_dtype=torch.bfloataa    ).to(__snake_case    )

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

     self.assertEqual(__snake_case,			__snake_case    )







from __future__ import annotations






def        lowerCamelCase_  (				UpperCAmelCase_				:  int						)    ->					list[int]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    [0] * max_one

	for i in range(0				,							UpperCAmelCase_						):
		_UpperCamelCase :		List[Any]								=    i

	for i in range(2				,							UpperCAmelCase_						):
		if totients[i] == i:
			for j in range(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						):
				totients[j] -= totients[j] // i

	return totients






def        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  int						)    ->					bool:




	'''simple docstring'''




	return sorted(str(UpperCAmelCase_						)						) == sorted(str(UpperCAmelCase_						)						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  int = 1_0_0_0_0_0_0_0						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		List[Any]								=    1  # i
	_UpperCamelCase :		List[str]								=    0  # φ(i)
	_UpperCamelCase :		str								=    get_totients(max_n + 1						)

	for i in range(2				,							max_n + 1						):
		_UpperCamelCase :		List[str]								=    totients[i]

		if i * min_denominator < min_numerator * t and has_same_digits(UpperCAmelCase_				,							UpperCAmelCase_						):
			_UpperCamelCase :		Optional[int]								=    i
			_UpperCamelCase :		Tuple								=    t

	return min_numerator


if __name__ == "__main__":
							print(f'{solution() = }')




