"""simple docstring"""


import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__  		=						get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
class        a				(		lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  Dict     							=							XLMProphetNetTokenizer
    _snake_case     :  Tuple     							=							False
    _snake_case     :  Tuple     							=							True


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      super().setUp()

      # We have a SentencePiece fixture for testing
      _UpperCAmelCase						 =      XLMProphetNetTokenizer(__lowerCAmelCase   ,				keep_accents=__lowerCAmelCase					)
      tokenizer.save_pretrained(self.tmpdirname					)


    def      lowerCAmelCase_  (					self	:  str					):
      _UpperCAmelCase						 =      """[PAD]"""
      _UpperCAmelCase						 =      0

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCAmelCase					)   ,				__lowerCAmelCase					)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCAmelCase					)   ,				__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      _UpperCAmelCase						 =      list(self.get_tokenizer().get_vocab().keys()					)

      self.assertEqual(vocab_keys[0]   ,				"""[PAD]"""					)
      self.assertEqual(vocab_keys[1]   ,				"""[CLS]"""					)
      self.assertEqual(vocab_keys[-1]   ,				"""j"""					)
      self.assertEqual(len(__lowerCAmelCase					)   ,				1012					)


    def      lowerCAmelCase_  (					self	:  str					):
      self.assertEqual(self.get_tokenizer().vocab_size   ,				1012					)


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      XLMProphetNetTokenizer(__lowerCAmelCase   ,				keep_accents=__lowerCAmelCase					)

      _UpperCAmelCase						 =      tokenizer.tokenize("""This is a test"""					)
      self.assertListEqual(__lowerCAmelCase   ,				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]					)

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__lowerCAmelCase					)   ,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]   ,				)

      _UpperCAmelCase						 =      tokenizer.tokenize("""I was born in 92000, and this is falsé."""					)
      self.assertListEqual(
          __lowerCAmelCase   ,				[
              SPIECE_UNDERLINE + """I""",
              SPIECE_UNDERLINE + """was""",
              SPIECE_UNDERLINE + """b""",
              """or""",
              """n""",
              SPIECE_UNDERLINE + """in""",
              SPIECE_UNDERLINE + """""",
              """9""",
              """2""",
              """0""",
              """0""",
              """0""",
              """,""",
              SPIECE_UNDERLINE + """and""",
              SPIECE_UNDERLINE + """this""",
              SPIECE_UNDERLINE + """is""",
              SPIECE_UNDERLINE + """f""",
              """al""",
              """s""",
              """é""",
              """.""",
          ]   ,				)
      _UpperCAmelCase						 =      tokenizer.convert_tokens_to_ids(__lowerCAmelCase					)
      self.assertListEqual(
          __lowerCAmelCase   ,				[
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
          ]   ,				)

      _UpperCAmelCase						 =      tokenizer.convert_ids_to_tokens(__lowerCAmelCase					)
      self.assertListEqual(
          __lowerCAmelCase   ,				[
              SPIECE_UNDERLINE + """I""",
              SPIECE_UNDERLINE + """was""",
              SPIECE_UNDERLINE + """b""",
              """or""",
              """n""",
              SPIECE_UNDERLINE + """in""",
              SPIECE_UNDERLINE + """""",
              """[UNK]""",
              """2""",
              """0""",
              """0""",
              """0""",
              """,""",
              SPIECE_UNDERLINE + """and""",
              SPIECE_UNDERLINE + """this""",
              SPIECE_UNDERLINE + """is""",
              SPIECE_UNDERLINE + """f""",
              """al""",
              """s""",
              """[UNK]""",
              """.""",
          ]   ,				)


    @cached_property
    def      lowerCAmelCase_  (					self	:  Tuple					):
      return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""					)


    @slow
    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      """Hello World!"""
      _UpperCAmelCase						 =      [3_5389, 6672, 49, 2]
      self.assertListEqual(__lowerCAmelCase   ,				self.big_tokenizer.encode(__lowerCAmelCase					)					)


    @slow
    def      lowerCAmelCase_  (					self	:  Tuple					):
      # fmt: off
      _UpperCAmelCase						 =      {"""input_ids""": [[1_1073, 8_2783, 18, 26, 8_2783, 549, 5_1540, 248, 1_7209, 1301, 217, 20, 21_5186, 1325, 147, 1_7209, 1301, 217, 20, 5_6370, 53, 12_2020, 20, 1_6477, 27, 8_7355, 4548, 20, 4728, 7_8392, 17, 15_9969, 18, 26, 2_4491, 629, 15, 538, 2_2704, 5439, 15, 2788, 2_4491, 9885, 15, 4_3534, 605, 15, 814, 1_8403, 3_3200, 29, 15, 4_3534, 2_4458, 1_2410, 111, 2_4966, 8_3669, 9637, 14_4068, 26, 850, 2_2346, 27, 147, 2_4966, 8_3669, 8_3490, 26, 3_9113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 12_2020, 11_5785, 34, 816, 1339, 4_6887, 18, 147, 5_3905, 1951, 4_2238, 4_1170, 1_7732, 834, 436, 15, 2_7523, 9_8733, 217, 147, 5542, 4981, 930, 1_7347, 16, 2], [2_0091, 629, 94, 8_2786, 58, 490, 20, 1528, 84, 5_3905, 344, 8_0592, 11_0128, 1_8822, 5267, 1306, 62, 15_2537, 308, 7997, 401, 12_4427, 549, 3_5442, 225, 109, 1_5055, 2_5748, 147, 7119, 4_3712, 34, 767, 13_5366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 6_3784, 11_9466, 17, 14_7808, 8_8214, 18, 656, 81, 32, 3296, 1_0280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=__lowerCAmelCase   ,				model_name="""microsoft/xprophetnet-large-wiki100-cased"""   ,				revision="""1acad1643ddd54a44df6a1b797ada8373685d90e"""   ,				)

"""simple docstring"""


import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class        a				(		lowerCAmelCase_	,							lowerCAmelCase_	,							lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  Optional[int]     							=							StableUnCLIPPipeline
    _snake_case     :  List[str]     							=							TEXT_TO_IMAGE_PARAMS
    _snake_case     :  Tuple     							=							TEXT_TO_IMAGE_BATCH_PARAMS
    _snake_case     :  List[Any]     							=							TEXT_TO_IMAGE_IMAGE_PARAMS
    _snake_case     :  str     							=							TEXT_TO_IMAGE_IMAGE_PARAMS

    # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
    _snake_case     :  str     							=							False


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      32
      _UpperCAmelCase						 =      embedder_hidden_size

      # prior components

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=__lowerCAmelCase   ,				projection_dim=__lowerCAmelCase   ,				intermediate_size=37   ,				layer_norm_eps=1e-0_5   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)					)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      PriorTransformer(
          num_attention_heads=2   ,				attention_head_dim=12   ,				embedding_dim=__lowerCAmelCase   ,				num_layers=1   ,				)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      DDPMScheduler(
          variance_type="""fixed_small_log"""   ,				prediction_type="""sample"""   ,				num_train_timesteps=1000   ,				clip_sample=__lowerCAmelCase   ,				clip_sample_range=5.0   ,				beta_schedule="""squaredcos_cap_v2"""   ,				)

      # regular denoising components

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      StableUnCLIPImageNormalizer(embedding_dim=__lowerCAmelCase					)
      _UpperCAmelCase						 =      DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""					)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=__lowerCAmelCase   ,				projection_dim=32   ,				intermediate_size=37   ,				layer_norm_eps=1e-0_5   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)					)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      UNetaDConditionModel(
          sample_size=32   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")   ,				up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")   ,				block_out_channels=(32, 64)   ,				attention_head_dim=(2, 4)   ,				class_embed_type="""projection"""   ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2   ,				cross_attention_dim=__lowerCAmelCase   ,				layers_per_block=1   ,				upcast_attention=__lowerCAmelCase   ,				use_linear_projection=__lowerCAmelCase   ,				)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      DDIMScheduler(
          beta_schedule="""scaled_linear"""   ,				beta_start=0.00_085   ,				beta_end=0.012   ,				prediction_type="""v_prediction"""   ,				set_alpha_to_one=__lowerCAmelCase   ,				steps_offset=1   ,				)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      AutoencoderKL()

      _UpperCAmelCase						 =      {
          # prior components
          """prior_tokenizer""": prior_tokenizer,
          """prior_text_encoder""": prior_text_encoder,
          """prior""": prior,
          """prior_scheduler""": prior_scheduler,
          # image noising components
          """image_normalizer""": image_normalizer,
          """image_noising_scheduler""": image_noising_scheduler,
          # regular denoising components
          """tokenizer""": tokenizer,
          """text_encoder""": text_encoder,
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
      }

      return components


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  int=0					):
      if str(__lowerCAmelCase					).startswith("""mps"""					):
        _UpperCAmelCase						 =      torch.manual_seed(__lowerCAmelCase					)
      else:
        _UpperCAmelCase						 =      torch.Generator(device=__lowerCAmelCase					).manual_seed(__lowerCAmelCase					)
      _UpperCAmelCase						 =      {
          """prompt""": """A painting of a squirrel eating a burger""",
          """generator""": generator,
          """num_inference_steps""": 2,
          """prior_num_inference_steps""": 2,
          """output_type""": """numpy""",
      }
      return inputs


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      torch_device == """cpu"""

      self._test_attention_slicing_forward_pass(test_max_difference=__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      torch_device in ["""cpu""", """mps"""]

      self._test_inference_batch_single_identical(test_max_difference=__lowerCAmelCase					)



@slow
@require_torch_gpu
class        a				(		unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  List[str]					):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""					)

      _UpperCAmelCase						 =      StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""   ,				torch_dtype=torch.floataa					)
      pipe.to(__lowerCAmelCase					)
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)
      # stable unclip will oom when integration tests are run on a V100,
      # so turn on memory savings
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _UpperCAmelCase						 =      torch.Generator(device="""cpu"""					).manual_seed(0					)
      _UpperCAmelCase						 =      pipe("""anime turle"""   ,				generator=__lowerCAmelCase   ,				output_type="""np"""					)

      _UpperCAmelCase						 =      output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__lowerCAmelCase   ,				__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      _UpperCAmelCase						 =      StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""   ,				torch_dtype=torch.floataa					)
      _UpperCAmelCase						 =      pipe.to(__lowerCAmelCase					)
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _UpperCAmelCase						 =      pipe(
          """anime turtle"""   ,				prior_num_inference_steps=2   ,				num_inference_steps=2   ,				output_type="""np"""   ,				)

      _UpperCAmelCase						 =      torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 10**9
