



"""simple docstring"""






import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor




class    __snake_case(					unittest.TestCase       ):




    @property
    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			1
      _SCREAMING_SNAKE_CASE        =			3
      _SCREAMING_SNAKE_CASE        =			8
      _SCREAMING_SNAKE_CASE        =			8

      _SCREAMING_SNAKE_CASE        =			torch.rand((batch_size, num_channels, height, width)			)

      return sample




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      if isinstance(image[0]    ,							PIL.Image.Image			):
        return np.stack([np.array(A_			) for i in image]    ,							axis=0			)
      elif isinstance(A_    ,							torch.Tensor			):
        return image.cpu().numpy().transpose(0    ,							2    ,							3    ,							1			)
      return image




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			VaeImageProcessor(do_resize=A_    ,							do_normalize=A_			)

      _SCREAMING_SNAKE_CASE        =			self.dummy_sample
      _SCREAMING_SNAKE_CASE        =			self.to_np(A_			)

      for output_type in ["pt", "np", "pil"]:
        _SCREAMING_SNAKE_CASE        =			image_processor.postprocess(
            image_processor.preprocess(A_			)    ,							output_type=A_    ,							)
        _SCREAMING_SNAKE_CASE        =			self.to_np(A_			)
        _SCREAMING_SNAKE_CASE        =			(input_np * 255).round() if output_type == '''pil''' else input_np
        assert (
            np.abs(in_np - out_np			).max() < 1e-6
        ), F'''decoded output does not match input for output_type {output_type}'''




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			VaeImageProcessor(do_resize=A_    ,							do_normalize=A_			)
      _SCREAMING_SNAKE_CASE        =			self.dummy_sample.cpu().numpy().transpose(0    ,							2    ,							3    ,							1			)

      for output_type in ["pt", "np", "pil"]:
        _SCREAMING_SNAKE_CASE        =			image_processor.postprocess(image_processor.preprocess(A_			)    ,							output_type=A_			)

        _SCREAMING_SNAKE_CASE        =			self.to_np(A_			)
        _SCREAMING_SNAKE_CASE        =			(input_np * 255).round() if output_type == '''pil''' else input_np
        assert (
            np.abs(in_np - out_np			).max() < 1e-6
        ), F'''decoded output does not match input for output_type {output_type}'''




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			VaeImageProcessor(do_resize=A_    ,							do_normalize=A_			)

      _SCREAMING_SNAKE_CASE        =			self.dummy_sample.cpu().numpy().transpose(0    ,							2    ,							3    ,							1			)
      _SCREAMING_SNAKE_CASE        =			image_processor.numpy_to_pil(A_			)

      for output_type in ["pt", "np", "pil"]:
        _SCREAMING_SNAKE_CASE        =			image_processor.postprocess(image_processor.preprocess(A_			)    ,							output_type=A_			)
        for i, o in zip(A_    ,							A_			):
          _SCREAMING_SNAKE_CASE        =			np.array(A_			)
          _SCREAMING_SNAKE_CASE        =			self.to_np(A_			) if output_type == '''pil''' else (self.to_np(A_			) * 255).round()
          assert (
              np.abs(in_np - out_np			).max() < 1e-6
          ), F'''decoded output does not match input for output_type {output_type}'''




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			VaeImageProcessor(do_resize=A_    ,							do_normalize=A_			)

      _SCREAMING_SNAKE_CASE        =			self.dummy_sample
      _SCREAMING_SNAKE_CASE        =			input_pt_ad.squeeze(0			)

      _SCREAMING_SNAKE_CASE        =			image_processor.postprocess(
          image_processor.preprocess(A_			)    ,							output_type='''np'''    ,							)
      _SCREAMING_SNAKE_CASE        =			image_processor.postprocess(
          image_processor.preprocess(A_			)    ,							output_type='''np'''    ,							)

      _SCREAMING_SNAKE_CASE        =			self.to_np(self.dummy_sample			)
      _SCREAMING_SNAKE_CASE        =			input_np_ad.squeeze(0			)

      _SCREAMING_SNAKE_CASE        =			image_processor.postprocess(
          image_processor.preprocess(A_			)    ,							output_type='''np'''    ,							)
      _SCREAMING_SNAKE_CASE        =			image_processor.postprocess(
          image_processor.preprocess(A_			)    ,							output_type='''np'''    ,							)

      assert np.abs(out_pt_ad - out_pt_ad			).max() < 1e-6
      assert np.abs(out_np_ad - out_np_ad			).max() < 1e-6




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			VaeImageProcessor(do_resize=A_    ,							do_normalize=A_			)

      _SCREAMING_SNAKE_CASE        =			self.dummy_sample
      _SCREAMING_SNAKE_CASE        =			list(A_			)

      _SCREAMING_SNAKE_CASE        =			image_processor.postprocess(
          image_processor.preprocess(A_			)    ,							output_type='''np'''    ,							)

      _SCREAMING_SNAKE_CASE        =			image_processor.postprocess(
          image_processor.preprocess(A_			)    ,							output_type='''np'''    ,							)

      _SCREAMING_SNAKE_CASE        =			self.to_np(self.dummy_sample			)
      list(A_			)

      _SCREAMING_SNAKE_CASE        =			image_processor.postprocess(
          image_processor.preprocess(A_			)    ,							output_type='''np'''    ,							)

      _SCREAMING_SNAKE_CASE        =			image_processor.postprocess(
          image_processor.preprocess(A_			)    ,							output_type='''np'''    ,							)

      assert np.abs(out_pt_ad - out_pt_list			).max() < 1e-6
      assert np.abs(out_np_ad - out_np_list			).max() < 1e-6





"""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:
    lowerCamelCase				:     str								=  False




class    __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    ,							):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			parent
      _SCREAMING_SNAKE_CASE        =			batch_size
      _SCREAMING_SNAKE_CASE        =			seq_length
      _SCREAMING_SNAKE_CASE        =			is_training
      _SCREAMING_SNAKE_CASE        =			use_token_type_ids
      _SCREAMING_SNAKE_CASE        =			use_input_mask
      _SCREAMING_SNAKE_CASE        =			use_labels
      _SCREAMING_SNAKE_CASE        =			use_mc_token_ids
      _SCREAMING_SNAKE_CASE        =			vocab_size
      _SCREAMING_SNAKE_CASE        =			hidden_size
      _SCREAMING_SNAKE_CASE        =			num_hidden_layers
      _SCREAMING_SNAKE_CASE        =			intermediate_size
      _SCREAMING_SNAKE_CASE        =			hidden_act
      _SCREAMING_SNAKE_CASE        =			hidden_dropout_prob
      _SCREAMING_SNAKE_CASE        =			attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE        =			max_position_embeddings
      _SCREAMING_SNAKE_CASE        =			type_vocab_size
      _SCREAMING_SNAKE_CASE        =			type_sequence_label_size
      _SCREAMING_SNAKE_CASE        =			num_labels
      _SCREAMING_SNAKE_CASE        =			num_choices
      _SCREAMING_SNAKE_CASE        =			scope
      _SCREAMING_SNAKE_CASE        =			vocab_size - 1
      _SCREAMING_SNAKE_CASE        =			vocab_size - 1
      _SCREAMING_SNAKE_CASE        =			vocab_size - 1




    def   A   (   self			):





      '''simple docstring'''



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




    def   A   (   self    ,							A_=False    ,							A_=False    ,							A_=False			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size			)

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

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

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

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

      _SCREAMING_SNAKE_CASE        =			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   A   (   self    ,							A_=False    ,							A_=False    ,							A_=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=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   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.get_config()
      _SCREAMING_SNAKE_CASE        =			300
      return config




    def   A   (   self			):





      '''simple docstring'''



      (
          (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     
      )        =			self.prepare_config_and_inputs()

      _SCREAMING_SNAKE_CASE        =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
      _SCREAMING_SNAKE_CASE        =			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   A   (   self    ,							A_    ,							A_    ,							A_    ,							A_    ,							A_    ,							*A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			True
      _SCREAMING_SNAKE_CASE        =			RwkvModel(config=A_			)
      model.to(A_			)
      model.eval()

      _SCREAMING_SNAKE_CASE        =			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   A   (   self    ,							A_    ,							A_    ,							A_    ,							A_    ,							A_    ,							*A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			RwkvForCausalLM(A_			)
      model.to(A_			)
      model.eval()

      _SCREAMING_SNAKE_CASE        =			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   A   (   self    ,							A_    ,							A_    ,							A_    ,							A_    ,							A_    ,							*A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			RwkvModel(config=A_			)
      model.to(A_			)
      model.eval()

      _SCREAMING_SNAKE_CASE        =			model(A_			)
      _SCREAMING_SNAKE_CASE        =			outputs.last_hidden_state

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

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

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




    def   A   (   self    ,							A_    ,							A_    ,							A_    ,							A_    ,							A_    ,							*A_    ,							A_=False			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			RwkvForCausalLM(A_			)
      model.to(A_			)
      if gradient_checkpointing:
        model.gradient_checkpointing_enable()

      _SCREAMING_SNAKE_CASE        =			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   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.prepare_config_and_inputs()

      (
          (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     (
          _SCREAMING_SNAKE_CASE
      )    ,     
      )        =			config_and_inputs

      _SCREAMING_SNAKE_CASE        =			{'''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(					__A			,			__A			,			__A			,			unittest.TestCase       ):
    _A       = (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
    _A       = (
        {'''feature-extraction''': RwkvModel, '''text-generation''': RwkvForCausalLM} if is_torch_available() else {}
    )
    # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
    _A       = False
    _A       = False
    _A       = False
    _A       = False
    _A       = False  # Rwkv does not support head masking




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			RwkvModelTester(self			)
      _SCREAMING_SNAKE_CASE        =			ConfigTester(
          self    ,							config_class=A_    ,							n_embd=37    ,							common_properties=['''hidden_size''', '''num_hidden_layers''']			)




    def   A   (   self    ,							A_    ,							A_    ,							A_=None			):





      '''simple docstring'''



      if isinstance(A_    ,							torch.Tensor			):
        _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			member.max().item(), member.min().item()
      elif isinstance(A_    ,							A_			) or isinstance(A_    ,							A_			):
        _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			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'''			)

      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			container

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

      if not is_inside_interval:
        _SCREAMING_SNAKE_CASE        =			'''%s not found in %s''' % (safe_repr(A_			), safe_repr(A_			))
        self.fail(self._formatMessage(A_    ,							A_			)			)




    def   A   (   self			):





      '''simple docstring'''



      self.config_tester.run_common_tests()




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_rwkv_model(*A_			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_causl_lm(*A_			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_state_equivalency(*A_			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _SCREAMING_SNAKE_CASE        =			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   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.model_tester.prepare_config_and_inputs_for_common()
      _SCREAMING_SNAKE_CASE        =			True

      _SCREAMING_SNAKE_CASE        =			getattr(self.model_tester    ,							'''seq_length'''    ,							A_			)

      for model_class in self.all_model_classes:
        _SCREAMING_SNAKE_CASE        =			True
        _SCREAMING_SNAKE_CASE        =			False
        _SCREAMING_SNAKE_CASE        =			True
        _SCREAMING_SNAKE_CASE        =			model_class(A_			)
        model.to(A_			)
        model.eval()

        _SCREAMING_SNAKE_CASE        =			self._prepare_for_class(A_    ,							A_			)
        _SCREAMING_SNAKE_CASE        =			inputs['''input_ids'''].shape[0]
        with torch.no_grad():
          _SCREAMING_SNAKE_CASE        =			model(**A_			)
        _SCREAMING_SNAKE_CASE        =			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"]
        _SCREAMING_SNAKE_CASE        =			True
        _SCREAMING_SNAKE_CASE        =			model_class(A_			)
        model.to(A_			)
        model.eval()

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

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

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

        _SCREAMING_SNAKE_CASE        =			1
        self.assertEqual(out_len + added_hidden_states    ,							len(A_			)			)

        _SCREAMING_SNAKE_CASE        =			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   A   (   self			):





      '''simple docstring'''



      for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _SCREAMING_SNAKE_CASE        =			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       ):




    def   A   (   self			):





      '''simple docstring'''



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




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''Hello my name is Jasmine and I am a newbie to the'''
      _SCREAMING_SNAKE_CASE        =			RwkvForCausalLM.from_pretrained(self.model_id			).to(A_			)

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

      self.assertEqual(A_    ,							A_			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''Hello my name is Jasmine and I am a newbie to the'''

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

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

      self.assertEqual(A_    ,							A_			)
