






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFXLMRobertaModel






@require_tf
@require_sentencepiece
@require_tokenizers
class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       @slow
       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              =TFXLMRobertaModel.from_pretrained('jplu/tf-xlm-roberta-base')

          _lowercase              ={
              'input_ids': tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]],      dtype=tf.intaa),  # "My dog is cute"
              'attention_mask': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]],      dtype=tf.intaa),
          }

          _lowercase              =model(snake_case)['last_hidden_state']
          _lowercase              =tf.TensorShape((1, 6, 768))
          self.assertEqual(output.shape,      snake_case)
          # compare the actual values for a slice.
          _lowercase              =tf.convert_to_tensor(
              [
                  [
                      [0.0_6_8_1_7_6_2, 0.1_0_8_9_4_4_5_1, 0.0_6_7_7_2_5_0_4],
                      [-0.0_6_4_2_3_6_6_8, 0.0_2_3_6_6_6_1_5, 0.0_4_3_2_9_3_4_4],
                      [-0.0_6_0_5_7_2_9_5, 0.0_9_9_7_4_1_3_5, -0.0_0_0_7_0_5_8_4],
                  ]
              ],      dtype=tf.floataa,      )

          self.assertTrue(np.allclose(output[:, :3, :3].numpy(),      expected_slice.numpy(),      atol=1e-4))










from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Union[List[PIL.Image.Image], np.ndarray]
       __lowerCAmelCase    :		Optional[List[bool]]


if is_transformers_available() and is_torch_available():
    from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


