code: 
  
  '''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:
         snake_case_    						=				False
  
  
  class    SCREAMING_SNAKE_CASE__      :
  
  
  
  
  
      def __init__(   self		,     a		,     a=14		,     a=7		,     a=True		,     a=False		,     a=True		,     a=True		,     a=True		,     a=99		,     a=32		,     a=5		,     a=37		,     a="gelu"		,     a=0.1		,     a=0.1		,     a=512		,     a=16		,     a=2		,     a=3		,     a=4		,     a=None		,     ):
           lowercase__					:     Tuple   = parent
           lowercase__					:     Optional[int]   = batch_size
           lowercase__					:     Tuple   = seq_length
           lowercase__					:     Tuple   = is_training
           lowercase__					:     str   = use_token_type_ids
           lowercase__					:     Union[str, Any]   = use_input_mask
           lowercase__					:     List[str]   = use_labels
           lowercase__					:     List[Any]   = use_mc_token_ids
           lowercase__					:     int   = vocab_size
           lowercase__					:     str   = hidden_size
           lowercase__					:     List[Any]   = num_hidden_layers
           lowercase__					:     Optional[Any]   = intermediate_size
           lowercase__					:     Tuple   = hidden_act
           lowercase__					:     Optional[Any]   = hidden_dropout_prob
           lowercase__					:     List[str]   = attention_probs_dropout_prob
           lowercase__					:     str   = max_position_embeddings
           lowercase__					:     Tuple   = type_vocab_size
           lowercase__					:     Optional[Any]   = type_sequence_label_size
           lowercase__					:     int   = num_labels
           lowercase__					:     List[str]   = num_choices
           lowercase__					:     int   = scope
           lowercase__					:     Tuple   = vocab_size - 1
           lowercase__					:     int   = vocab_size - 1
           lowercase__					:     Any   = vocab_size - 1
  
  
  
  
  
      def    snake_case_       (   self):
           return RwkvConfig.from_pretrained('sgugger/rwkv-4-pile-7b')
  
  
  
  
  
      def    snake_case_       (   self		,     a=False		,     a=False		,     a=False):
           lowercase__					:     str   = ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size)
  
           lowercase__					:     int   = None
           if self.use_input_mask:
                lowercase__					:     int   = random_attention_mask([self.batch_size, self.seq_length])
  
           lowercase__					:     str   = None
           if self.use_token_type_ids:
                lowercase__					:     Optional[Any]   = ids_tensor([self.batch_size, self.seq_length]		,     self.type_vocab_size)
  
           lowercase__					:     Optional[int]   = None
           if self.use_mc_token_ids:
                lowercase__					:     Optional[int]   = ids_tensor([self.batch_size, self.num_choices]		,     self.seq_length)
  
           lowercase__					:     Optional[int]   = None
           lowercase__					:     Tuple   = None
           lowercase__					:     Tuple   = None
           if self.use_labels:
                lowercase__					:     Dict   = ids_tensor([self.batch_size]		,     self.type_sequence_label_size)
                lowercase__					:     Optional[Any]   = ids_tensor([self.batch_size, self.seq_length]		,     self.num_labels)
                lowercase__					:     str   = ids_tensor([self.batch_size]		,     self.num_choices)
  
           lowercase__					:     List[str]   = 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    snake_case_       (   self		,     a=False		,     a=False		,     a=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    snake_case_       (   self):
           lowercase__					:     Optional[int]   = self.get_config()
           lowercase__					:     Union[str, Any]   = 300
           return config
  
  
  
  
  
      def    snake_case_       (   self):
           (
               lowercase__				
           )					:     int   = self.prepare_config_and_inputs()
  
           lowercase__					:     List[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    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a		,     *a):
           lowercase__					:     Any   = True
           lowercase__					:     Dict   = RwkvModel(config=a)
           model.to(a)
           model.eval()
  
           lowercase__					:     Dict   = 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    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a		,     *a):
           lowercase__					:     Any   = RwkvForCausalLM(a)
           model.to(a)
           model.eval()
  
           lowercase__					:     Union[str, Any]   = 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    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a		,     *a):
           lowercase__					:     Optional[int]   = RwkvModel(config=a)
           model.to(a)
           model.eval()
  
           lowercase__					:     List[str]   = model(a)
           lowercase__					:     int   = outputs.last_hidden_state
  
           lowercase__					:     Optional[int]   = model(input_ids[:, :2])
           lowercase__					:     Optional[int]   = outputs.last_hidden_state
  
           # Using the state computed on the first inputs, we will get the same output
           lowercase__					:     int   = model(input_ids[:, 2:]		,     state=outputs.state)
           lowercase__					:     List[Any]   = outputs.last_hidden_state
  
           self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]		,     dim=1)		,     a		,     atol=1e-5))
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a		,     *a		,     a=False):
           lowercase__					:     Optional[int]   = RwkvForCausalLM(a)
           model.to(a)
           if gradient_checkpointing:
                model.gradient_checkpointing_enable()
  
           lowercase__					:     Tuple   = 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    snake_case_       (   self):
           lowercase__					:     List[Any]   = self.prepare_config_and_inputs()
  
           (
               lowercase__				
           )					:     Optional[int]   = config_and_inputs
  
           lowercase__					:     int   = {'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    SCREAMING_SNAKE_CASE__      (__snake_case	, __snake_case	, __snake_case	, unittest.TestCase  ):
      __lowerCamelCase		: List[Any]														=  (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
      __lowerCamelCase		: Tuple														=  (
          {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
      )
      # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
      __lowerCamelCase		: Optional[Any]														=  False
      __lowerCamelCase		: Any														=  False
      __lowerCamelCase		: List[str]														=  False
      __lowerCamelCase		: int														=  False
      __lowerCamelCase		: List[str]														=  False  # Rwkv does not support head masking
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Dict   = RwkvModelTester(self)
           lowercase__					:     List[Any]   = ConfigTester(
               self		,     config_class=a		,     n_embd=37		,     common_properties=['hidden_size', 'num_hidden_layers'])
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a=None):
           if isinstance(a		,     torch.Tensor):
                lowercase__					:     Optional[Any]   = member.max().item(), member.min().item()
           elif isinstance(a		,     a) or isinstance(a		,     a):
                lowercase__					:     Optional[int]   = 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')
  
           lowercase__					:     Union[str, Any]   = container
  
           lowercase__					:     str   = (min_value >= expected_min) and (max_value <= expected_max)
  
           if not is_inside_interval:
                lowercase__					:     Tuple   = '%s not found in %s' % (safe_repr(a), safe_repr(a))
                self.fail(self._formatMessage(a		,     a))
  
  
  
  
  
      def    snake_case_       (   self):
           self.config_tester.run_common_tests()
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     str   = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_rwkv_model(*a)
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Dict   = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_causl_lm(*a)
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     int   = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_state_equivalency(*a)
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     str   = self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
                lowercase__					:     Union[str, Any]   = 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    snake_case_       (   self):
           lowercase__					:     Optional[Any]   = self.model_tester.prepare_config_and_inputs_for_common()
           lowercase__					:     Optional[Any]   = True
  
           lowercase__					:     List[Any]   = getattr(self.model_tester		,     'seq_length'		,     a)
  
           for model_class in self.all_model_classes:
                lowercase__					:     Tuple   = True
                lowercase__					:     Optional[Any]   = False
                lowercase__					:     Optional[Any]   = True
                lowercase__					:     Any   = model_class(a)
                model.to(a)
                model.eval()
  
                lowercase__					:     Optional[Any]   = self._prepare_for_class(a		,     a)
                lowercase__					:     Dict   = inputs['input_ids'].shape[0]
                with torch.no_grad():
                     lowercase__					:     Optional[int]   = model(**a)
                lowercase__					:     List[str]   = 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"]
                lowercase__					:     Optional[Any]   = True
                lowercase__					:     Any   = model_class(a)
                model.to(a)
                model.eval()
  
                lowercase__					:     int   = self._prepare_for_class(a		,     a)
                lowercase__					:     Dict   = inputs['input_ids'].shape[0]
                with torch.no_grad():
                     lowercase__					:     List[Any]   = model(**a)
                lowercase__					:     List[Any]   = 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]		,     )
                lowercase__					:     int   = len(a)
  
                # Check attention is always last and order is fine
                lowercase__					:     Dict   = True
                lowercase__					:     Dict   = True
                lowercase__					:     Dict   = model_class(a)
                model.to(a)
                model.eval()
  
                lowercase__					:     List[str]   = self._prepare_for_class(a		,     a)
                lowercase__					:     Any   = inputs['input_ids'].shape[0]
                with torch.no_grad():
                     lowercase__					:     int   = model(**a)
  
                lowercase__					:     Any   = 1
                self.assertEqual(out_len + added_hidden_states		,     len(a))
  
                lowercase__					:     int   = 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    snake_case_       (   self):
           for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                lowercase__					:     Union[str, Any]   = 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    SCREAMING_SNAKE_CASE__      (unittest.TestCase  ):
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Any   = 'RWKV/rwkv-4-169m-pile'
           lowercase__					:     int   = AutoTokenizer.from_pretrained(self.model_id)
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Optional[int]   = 'Hello my name is Jasmine and I am a newbie to the'
           lowercase__					:     Optional[Any]   = RwkvForCausalLM.from_pretrained(self.model_id).to(a)
  
           lowercase__					:     List[Any]   = self.tokenizer('Hello my name is'		,     return_tensors='pt').input_ids.to(a)
           lowercase__					:     Dict   = model.generate(a		,     max_new_tokens=10)
           lowercase__					:     int   = self.tokenizer.decode(output[0].tolist())
  
           self.assertEqual(a		,     a)
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     List[str]   = 'Hello my name is Jasmine and I am a newbie to the'
  
           lowercase__					:     str   = self.tokenizer('Hello my name is'		,     return_tensors='pt').input_ids.to(a)
           lowercase__					:     Optional[int]   = RwkvForCausalLM.from_pretrained(self.model_id		,     torch_dtype=torch.bfloataa).to(a)
  
           lowercase__					:     Optional[int]   = model.generate(a		,     max_new_tokens=10)
           lowercase__					:     Union[str, Any]   = self.tokenizer.decode(output[0].tolist())
  
           self.assertEqual(a		,     a)
  
  
style_context: 
  
  # as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.
  
  import os
  import sys
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow
  
  from .utils import load_json
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      @classmethod
      def    snake_case_       (   cls):
           return cls
  
  
  
  
  
      @slow
      @require_torch_gpu
      def    snake_case_       (   self):
           lowercase__					:     List[Any]   = self.get_auto_remove_tmp_dir()
           lowercase__					:     List[str]   = f"""
              --model_name Helsinki-NLP/opus-mt-en-ro
              --save_dir {output_dir}
              --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
              --num_beams 2
              --task translation
          """.split()
  
           # we want this test to run even if there is only one GPU, but if there are more we use them all
           lowercase__					:     int   = get_gpu_count()
           lowercase__					:     Tuple   = f"""
              -m torch.distributed.launch
              --nproc_per_node={n_gpu}
              {self.test_file_dir}/run_distributed_eval.py
          """.split()
           lowercase__					:     Optional[Any]   = [sys.executable] + distributed_args + args
           execute_subprocess_async(a		,     env=self.get_env())
  
           lowercase__					:     str   = os.path.join(a		,     'test_bleu.json')
           lowercase__					:     List[str]   = load_json(a)
           # print(metrics)
           self.assertGreaterEqual(metrics['bleu']		,     25)
  
  