


"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel





@require_sentencepiece
@require_tokenizers
@require_torch
class   __a      (   unittest.TestCase				):
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Any        =       XLMRobertaModel.from_pretrained('xlm-roberta-base'			)
        lowercase__:       Optional[int]        =       torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]			)
        # The dog is cute and lives in the garden house

        lowercase__:       Union[str, Any]        =       torch.Size((1, 12, 768)			)  # batch_size, sequence_length, embedding_vector_dim
        lowercase__:       Dict        =       torch.tensor(
            [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]			)
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
               lowercase__:       Union[str, Any]        =       model(_snake_case			)['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,			_snake_case			)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,			_snake_case    ,			atol=1E-3			)			)
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Any        =       XLMRobertaModel.from_pretrained('xlm-roberta-large'			)
        lowercase__:       List[Any]        =       torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]			)
        # The dog is cute and lives in the garden house

        lowercase__:       Dict        =       torch.Size((1, 12, 1_024)			)  # batch_size, sequence_length, embedding_vector_dim
        lowercase__:       Optional[Any]        =       torch.tensor(
            [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]			)
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
               lowercase__:       Union[str, Any]        =       model(_snake_case			)['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,			_snake_case			)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,			_snake_case    ,			atol=1E-3			)			)






import torch

from diffusers import StableDiffusionPipeline


__lowerCAmelCase			=							'''path-to-your-trained-model'''
__lowerCAmelCase			=							StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

__lowerCAmelCase			=							'''A photo of sks dog in a bucket'''
__lowerCAmelCase			=							pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')


