





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:
    a__							:							Optional[int]			=False

class 					snake_case					:




 """simple docstring"""



 def __init__(	self	:							List[str]							,     __A	:							int							,     __A	:							Union[str, Any]=1_4							,     __A	:							Tuple=7							,     __A	:							List[Any]=True							,     __A	:							Dict=False							,     __A	:							Tuple=True							,     __A	:							Union[str, Any]=True							,     __A	:							List[Any]=True							,     __A	:							Any=9_9							,     __A	:							str=3_2							,     __A	:							Any=5							,     __A	:							List[str]=3_7							,     __A	:							List[str]="gelu"							,     __A	:							Union[str, Any]=0.1							,     __A	:							Tuple=0.1							,     __A	:							Dict=5_1_2							,     __A	:							Union[str, Any]=1_6							,     __A	:							List[Any]=2							,     __A	:							Dict=3							,     __A	:							str=4							,     __A	:							Any=None							,     ):
        __UpperCamelCase	=  parent
        __UpperCamelCase	=  batch_size
        __UpperCamelCase	=  seq_length
        __UpperCamelCase	=  is_training
        __UpperCamelCase	=  use_token_type_ids
        __UpperCamelCase	=  use_input_mask
        __UpperCamelCase	=  use_labels
        __UpperCamelCase	=  use_mc_token_ids
        __UpperCamelCase	=  vocab_size
        __UpperCamelCase	=  hidden_size
        __UpperCamelCase	=  num_hidden_layers
        __UpperCamelCase	=  intermediate_size
        __UpperCamelCase	=  hidden_act
        __UpperCamelCase	=  hidden_dropout_prob
        __UpperCamelCase	=  attention_probs_dropout_prob
        __UpperCamelCase	=  max_position_embeddings
        __UpperCamelCase	=  type_vocab_size
        __UpperCamelCase	=  type_sequence_label_size
        __UpperCamelCase	=  num_labels
        __UpperCamelCase	=  num_choices
        __UpperCamelCase	=  scope
        __UpperCamelCase	=  vocab_size - 1
        __UpperCamelCase	=  vocab_size - 1
        __UpperCamelCase	=  vocab_size - 1






 def 	_lowerCamelCase (	self	:							str     ):
        return RwkvConfig.from_pretrained('sgugger/rwkv-4-pile-7b'     )






 def 	_lowerCamelCase (	self	:							int							,     __A	:							Optional[Any]=False							,     __A	:							Optional[Any]=False							,     __A	:							List[str]=False     ):
        __UpperCamelCase	=  ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size     )

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

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

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

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

        __UpperCamelCase	=  self.get_config(
            gradient_checkpointing=__A							,     scale_attn_by_inverse_layer_idx=__A							,     reorder_and_upcast_attn=__A							,     )

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






 def 	_lowerCamelCase (	self	:							int							,     __A	:							str=False							,     __A	:							List[str]=False							,     __A	:							Optional[Any]=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=__A							,     bos_token_id=self.bos_token_id							,     eos_token_id=self.eos_token_id							,     pad_token_id=self.pad_token_id							,     gradient_checkpointing=__A							,     scale_attn_by_inverse_layer_idx=__A							,     reorder_and_upcast_attn=__A							,     )






 def 	_lowerCamelCase (	self	:							List[str]     ):
        __UpperCamelCase	=  self.get_config()
        __UpperCamelCase	=  3_0_0
        return config






 def 	_lowerCamelCase (	self	:							int     ):
        (
            __UpperCamelCase       
        )	=  self.prepare_config_and_inputs()

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

        __UpperCamelCase	=  model(__A     )

        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	:							int							,     __A	:							str							,     __A	:							int							,     __A	:							List[Any]							,     __A	:							Tuple							,     __A	:							int							,     *__A	:							Dict     ):
        __UpperCamelCase	=  RwkvForCausalLM(__A     )
        model.to(__A     )
        model.eval()

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






 def 	_lowerCamelCase (	self	:							Optional[Any]							,     __A	:							int							,     __A	:							Tuple							,     __A	:							Optional[int]							,     __A	:							Optional[int]							,     __A	:							List[str]							,     *__A	:							Dict     ):
        __UpperCamelCase	=  RwkvModel(config=__A     )
        model.to(__A     )
        model.eval()

        __UpperCamelCase	=  model(__A     )
        __UpperCamelCase	=  outputs.last_hidden_state

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

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

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






 def 	_lowerCamelCase (	self	:							Union[str, Any]							,     __A	:							Tuple							,     __A	:							Any							,     __A	:							List[str]							,     __A	:							str							,     __A	:							int							,     *__A	:							int							,     __A	:							Union[str, Any]=False     ):
        __UpperCamelCase	=  RwkvForCausalLM(__A     )
        model.to(__A     )
        if gradient_checkpointing:
               model.gradient_checkpointing_enable()

        __UpperCamelCase	=  model(__A							,     labels=__A     )
        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	:							str     ):
        __UpperCamelCase	=  self.prepare_config_and_inputs()

        (
            __UpperCamelCase       
        )	=  config_and_inputs

        __UpperCamelCase	=  {"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					(   snake_case_      ,	snake_case_      ,	snake_case_      ,	unittest.TestCase	):




 """simple docstring"""



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






 def 	_lowerCamelCase (	self	:							List[str]     ):
        __UpperCamelCase	=  RwkvModelTester(self     )
        __UpperCamelCase	=  ConfigTester(
            self							,     config_class=__A							,     n_embd=3_7							,     common_properties=['hidden_size', 'num_hidden_layers']     )






 def 	_lowerCamelCase (	self	:							str							,     __A	:							Optional[Any]							,     __A	:							Optional[Any]							,     __A	:							Optional[Any]=None     ):


        if isinstance(__A							,     torch.Tensor     ):
               __UpperCamelCase	=  member.max().item(), member.min().item()
        elif isinstance(__A							,     __A     ) or isinstance(__A							,     __A     ):
               __UpperCamelCase	=  max(__A     ), min(__A     )

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

        __UpperCamelCase	=  container

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

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






 def 	_lowerCamelCase (	self	:							List[str]     ):
        self.config_tester.run_common_tests()






 def 	_lowerCamelCase (	self	:							Any     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_rwkv_model(*__A     )






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






 def 	_lowerCamelCase (	self	:							List[Any]     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_state_equivalency(*__A     )






 def 	_lowerCamelCase (	self	:							int     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               __UpperCamelCase	=  model_class(config=__A     )
               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	:							List[Any]     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase	=  True

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

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

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

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

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

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

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

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

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

               __UpperCamelCase	=  outputs.attentions

               self.assertEqual(len(__A     )							,     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]     ):


        for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCamelCase	=  RwkvModel.from_pretrained(__A     )
               self.assertIsNotNone(__A     )






@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 	_lowerCamelCase (	self	:							Any     ):
        __UpperCamelCase	=  "RWKV/rwkv-4-169m-pile"
        __UpperCamelCase	=  AutoTokenizer.from_pretrained(self.model_id     )






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

        __UpperCamelCase	=  self.tokenizer('Hello my name is'							,     return_tensors='pt'     ).input_ids.to(__A     )
        __UpperCamelCase	=  model.generate(__A							,     max_new_tokens=1_0     )
        __UpperCamelCase	=  self.tokenizer.decode(output[0].tolist()     )

        self.assertEqual(__A							,     __A     )






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

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

        __UpperCamelCase	=  model.generate(__A							,     max_new_tokens=1_0     )
        __UpperCamelCase	=  self.tokenizer.decode(output[0].tolist()     )

        self.assertEqual(__A							,     __A     )







'''simple docstring'''
def     lowercase__       (       __lowercase						:  str    ,  __lowercase						:  Tuple					)			->				str:



       """simple docstring"""






       sequence.append(__lowercase					)

       __UpperCamelCase	=  0
       while sequence[index] != target:
              index += 1

       sequence.pop()

       if index == len(__lowercase					):
              return None

       return index


if __name__ == "__main__":
    a__							:							Any			=input('''Enter numbers separated by comma:\n''').strip()
    a__							:							Any			=[int(item) for item in user_input.split(''',''')]

    a__							:							Tuple			=input('''Enter a single number to be found in the list:\n''')
    a__							:							List[Any]			=int(target_input)
    a__							:							List[Any]			=sentinel_linear_search(sequence, target)
    if result is not None:
        print(f'{target} found at positions: {result}')
    else:
        print('''Not found''')
