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 _a   :

	'''simple docstring'''



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

	def      __UpperCAmelCase(   self    ):
			return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b"    )

	def      __UpperCAmelCase(   self							, __UpperCAmelCase=False							, __UpperCAmelCase=False							, __UpperCAmelCase=False    ):
			__A							:    Optional[Any]         =      ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size    )

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

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

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

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

			__A							:    Optional[Any]         =      self.get_config(
			    gradient_checkpointing=__UpperCAmelCase							, scale_attn_by_inverse_layer_idx=__UpperCAmelCase							, reorder_and_upcast_attn=__UpperCAmelCase							, )

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

	def      __UpperCAmelCase(   self							, __UpperCAmelCase=False							, __UpperCAmelCase=False							, __UpperCAmelCase=False    ):
			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=__UpperCAmelCase							, bos_token_id=self.bos_token_id							, eos_token_id=self.eos_token_id							, pad_token_id=self.pad_token_id							, gradient_checkpointing=__UpperCAmelCase							, scale_attn_by_inverse_layer_idx=__UpperCAmelCase							, reorder_and_upcast_attn=__UpperCAmelCase							, )

	def      __UpperCAmelCase(   self    ):
			__A							:    str         =      self.get_config()
			__A							:    Optional[int]         =      300
			return config

	def      __UpperCAmelCase(   self    ):
			(
			    (
			    __A      
			)  ,   (
			    __A      
			)  ,   (
			    __A      
			)  ,   (
			    __A      
			)  ,   (
			    __A      
			)  ,   (
			    __A      
			)  ,   (
			    __A      
			)  ,   (
			    __A      
			)  ,   (
			    __A      
			)  ,         
			)							:    int         =      self.prepare_config_and_inputs()

			__A							:    Dict         =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
			__A							:    Union[str, 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							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, *__UpperCAmelCase    ):
			__A							:    List[str]         =      True
			__A							:    Optional[int]         =      RwkvModel(config=__UpperCAmelCase    )
			model.to(__UpperCAmelCase    )
			model.eval()

			__A							:    Union[str, Any]         =      model(__UpperCAmelCase    )

			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							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, *__UpperCAmelCase    ):
			__A							:    str         =      RwkvForCausalLM(__UpperCAmelCase    )
			model.to(__UpperCAmelCase    )
			model.eval()

			__A							:    int         =      model(__UpperCAmelCase							, labels=__UpperCAmelCase    )
			self.parent.assertEqual(result.loss.shape							, ()    )
			self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)    )

	def      __UpperCAmelCase(   self							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, *__UpperCAmelCase    ):
			__A							:    Optional[Any]         =      RwkvModel(config=__UpperCAmelCase    )
			model.to(__UpperCAmelCase    )
			model.eval()

			__A							:    Optional[Any]         =      model(__UpperCAmelCase    )
			__A							:    Optional[Any]         =      outputs.last_hidden_state

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

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

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

	def      __UpperCAmelCase(   self							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, *__UpperCAmelCase							, __UpperCAmelCase=False    ):
			__A							:    Optional[Any]         =      RwkvForCausalLM(__UpperCAmelCase    )
			model.to(__UpperCAmelCase    )
			if gradient_checkpointing:
					model.gradient_checkpointing_enable()

			__A							:    List[str]         =      model(__UpperCAmelCase							, labels=__UpperCAmelCase    )
			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    ):
			__A							:    List[str]         =      self.prepare_config_and_inputs()

			(
			    (
			    __A      
			)  ,   (
			    __A      
			)  ,   (
			    __A      
			)  ,   (
			    __A      
			)  ,   (
			    __A      
			)  ,   (
			    __A      
			)  ,   (
			    __A      
			)  ,   (
			    __A      
			)  ,   (
			    __A      
			)  ,         
			)							:    Optional[Any]         =      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					):

	'''simple docstring'''



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

	def      __UpperCAmelCase(   self    ):
			__A							:    List[str]         =      RwkvModelTester(self    )
			__A							:    int         =      ConfigTester(
			    self							, config_class=__UpperCAmelCase							, n_embd=37							, common_properties=["hidden_size", "num_hidden_layers"]    )

	def      __UpperCAmelCase(   self							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase=None    ):
			if isinstance(__UpperCAmelCase							, torch.Tensor    ):
					__A  ,   __A							:    Tuple         =      member.max().item(), member.min().item()
			elif isinstance(__UpperCAmelCase							, __UpperCAmelCase    ) or isinstance(__UpperCAmelCase							, __UpperCAmelCase    ):
					__A  ,   __A							:    List[str]         =      max(__UpperCAmelCase    ), min(__UpperCAmelCase    )

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

			__A  ,   __A							:    List[Any]         =      container

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

			if not is_inside_interval:
					__A							:    Dict         =      "%s not found in %s" % (safe_repr(__UpperCAmelCase    ), safe_repr(__UpperCAmelCase    ))
					self.fail(self._formatMessage(__UpperCAmelCase							, __UpperCAmelCase    )    )

	def      __UpperCAmelCase(   self    ):
			self.config_tester.run_common_tests()

	def      __UpperCAmelCase(   self    ):
			__A							:    Tuple         =      self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_rwkv_model(*__UpperCAmelCase    )

	def      __UpperCAmelCase(   self    ):
			__A							:    List[Any]         =      self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_causl_lm(*__UpperCAmelCase    )

	def      __UpperCAmelCase(   self    ):
			__A							:    Union[str, Any]         =      self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_state_equivalency(*__UpperCAmelCase    )

	def      __UpperCAmelCase(   self    ):
			__A  ,   __A							:    int         =      self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					__A							:    Any         =      model_class(config=__UpperCAmelCase    )
					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    ):
			__A  ,   __A							:    Any         =      self.model_tester.prepare_config_and_inputs_for_common()
			__A							:    Optional[Any]         =      True

			__A							:    Optional[Any]         =      getattr(self.model_tester							, "seq_length"							, __UpperCAmelCase    )

			for model_class in self.all_model_classes:
					__A							:    Tuple         =      True
					__A							:    Optional[Any]         =      False
					__A							:    Tuple         =      True
					__A							:    int         =      model_class(__UpperCAmelCase    )
					model.to(__UpperCAmelCase    )
					model.eval()

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

					# check that output_attentions also work using config
					del inputs_dict["output_attentions"]
					__A							:    List[Any]         =      True
					__A							:    Tuple         =      model_class(__UpperCAmelCase    )
					model.to(__UpperCAmelCase    )
					model.eval()

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

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

					# Check attention is always last and order is fine
					__A							:    Optional[Any]         =      True
					__A							:    Optional[int]         =      True
					__A							:    Optional[Any]         =      model_class(__UpperCAmelCase    )
					model.to(__UpperCAmelCase    )
					model.eval()

					__A							:    Optional[Any]         =      self._prepare_for_class(__UpperCAmelCase							, __UpperCAmelCase    )
					__A							:    str         =      inputs["input_ids"].shape[0]
					with torch.no_grad():
							__A							:    Tuple         =      model(**__UpperCAmelCase    )

					__A							:    Optional[int]         =      1
					self.assertEqual(out_len + added_hidden_states							, len(__UpperCAmelCase    )    )

					__A							:    Optional[Any]         =      outputs.attentions

					self.assertEqual(len(__UpperCAmelCase    )							, 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    ):
			for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					__A							:    Dict         =      RwkvModel.from_pretrained(__UpperCAmelCase    )
					self.assertIsNotNone(__UpperCAmelCase    )


@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					):

	'''simple docstring'''



	def      __UpperCAmelCase(   self    ):
			__A							:    str         =      "RWKV/rwkv-4-169m-pile"
			__A							:    int         =      AutoTokenizer.from_pretrained(self.model_id    )

	def      __UpperCAmelCase(   self    ):
			__A							:    Tuple         =      "Hello my name is Jasmine and I am a newbie to the"
			__A							:    Optional[int]         =      RwkvForCausalLM.from_pretrained(self.model_id    ).to(__UpperCAmelCase    )

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

			self.assertEqual(__UpperCAmelCase							, __UpperCAmelCase    )







	def      __UpperCAmelCase(   self    ):
			__A							:    Union[str, Any]         =      "Hello my name is Jasmine and I am a newbie to the"

			__A							:    Optional[Any]         =      self.tokenizer("Hello my name is"							, return_tensors="pt"    ).input_ids.to(__UpperCAmelCase    )
			__A							:    Union[str, Any]         =      RwkvForCausalLM.from_pretrained(self.model_id							, torch_dtype=torch.bfloataa    ).to(__UpperCAmelCase    )

			__A							:    List[str]         =      model.generate(__UpperCAmelCase							, max_new_tokens=10    )
			__A							:    Dict         =      self.tokenizer.decode(output[0].tolist()    )

			self.assertEqual(__UpperCAmelCase							, __UpperCAmelCase    )






def 							lowerCAmelCase(	__lowerCamelCase = 10**6						):

					if n <= 0:
										raise ValueError('Please enter an integer greater than 0'						)

					__a							=   list(range(n + 1						)						)
					for number in range(2      ,				n + 1						):
										if phi[number] == number:
															phi[number] -= 1
															for multiple in range(number * 2      ,				n + 1      ,				__lowerCamelCase						):
																				__a							=   (phi[multiple] // number) * (number - 1)

					__a							=   1
					for number in range(1      ,				n + 1						):
										if (answer / phi[answer]) < (number / phi[number]):
															__a							=   number

					return answer


if __name__ == "__main__":
	print(solution())




