import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  AutoencoderKL
  lowerCamelCase__   =  '''sample'''
  lowerCamelCase__   =  1E-2







  @property
  def       __A   (							self       :				Any      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     4
        SCREAMING_SNAKE_CASE_      =     3
        SCREAMING_SNAKE_CASE_      =     (32, 32)

        SCREAMING_SNAKE_CASE_      =     floats_tensor((batch_size, num_channels) + sizes      ).to(__magic_name__      )

        return {"sample": image}







  @property
  def       __A   (							self       :				int      )     ->		List[str]:
        return (3, 32, 32)







  @property
  def       __A   (							self       :				int      )     ->		List[str]:
        return (3, 32, 32)







  def       __A   (							self       :				List[Any]      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     {
            "block_out_channels": [32, 64],
            "in_channels": 3,
            "out_channels": 3,
            "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
            "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
            "latent_channels": 4,
        }
        SCREAMING_SNAKE_CASE_      =     self.dummy_input
        return init_dict, inputs_dict







  def       __A   (							self       :				List[str]      )     ->		Dict:
        pass







  def       __A   (							self       :				List[str]      )     ->		str:
        pass







  @unittest.skipIf(torch_device == "mps"     ,   "Gradient checkpointing skipped on MPS"      )
  def       __A   (							self       :				int      )     ->		List[str]:
        # enable deterministic behavior for gradient checkpointing
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self.prepare_init_args_and_inputs_for_common()
        SCREAMING_SNAKE_CASE_      =     self.model_class(**__magic_name__      )
        model.to(__magic_name__      )

        assert not model.is_gradient_checkpointing and model.training

        SCREAMING_SNAKE_CASE_      =     model(**__magic_name__      ).sample
        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
        # we won't calculate the loss and rather backprop on out.sum()
        model.zero_grad()

        SCREAMING_SNAKE_CASE_      =     torch.randn_like(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     (out - labels).mean()
        loss.backward()

        # re-instantiate the model now enabling gradient checkpointing
        SCREAMING_SNAKE_CASE_      =     self.model_class(**__magic_name__      )
        # clone model
        model_a.load_state_dict(model.state_dict()      )
        model_a.to(__magic_name__      )
        model_a.enable_gradient_checkpointing()

        assert model_a.is_gradient_checkpointing and model_a.training

        SCREAMING_SNAKE_CASE_      =     model_a(**__magic_name__      ).sample
        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
        # we won't calculate the loss and rather backprop on out.sum()
        model_a.zero_grad()
        SCREAMING_SNAKE_CASE_      =     (out_a - labels).mean()
        loss_a.backward()

        # compare the output and parameters gradients
        self.assertTrue((loss - loss_a).abs() < 1e-5      )
        SCREAMING_SNAKE_CASE_      =     dict(model.named_parameters()      )
        SCREAMING_SNAKE_CASE_      =     dict(model_a.named_parameters()      )
        for name, param in named_params.items():
              self.assertTrue(torch_all_close(param.grad.data     ,   named_params_a[name].grad.data     ,   atol=5e-5      )      )







  def       __A   (							self       :				str      )     ->		int:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"     ,   output_loading_info=__magic_name__      )
        self.assertIsNotNone(__magic_name__      )
        self.assertEqual(len(loading_info["missing_keys"]      )     ,   0      )

        model.to(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     model(**self.dummy_input      )

        assert image is not None, "Make sure output is not None"







  def       __A   (							self       :				Any      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"      )
        SCREAMING_SNAKE_CASE_      =     model.to(__magic_name__      )
        model.eval()

        if torch_device == "mps":
              SCREAMING_SNAKE_CASE_      =     torch.manual_seed(0      )
        else:
              SCREAMING_SNAKE_CASE_      =     torch.Generator(device=__magic_name__      ).manual_seed(0      )

        SCREAMING_SNAKE_CASE_      =     torch.randn(
            1     ,   model.config.in_channels     ,   model.config.sample_size     ,   model.config.sample_size     ,   generator=torch.manual_seed(0      )     ,   )
        SCREAMING_SNAKE_CASE_      =     image.to(__magic_name__      )
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_      =     model(__magic_name__     ,   sample_posterior=__magic_name__     ,   generator=__magic_name__      ).sample

        SCREAMING_SNAKE_CASE_      =     output[0, -1, -3:, -3:].flatten().cpu()

        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
        # the expected output slices are not the same for CPU and GPU.
        if torch_device == "mps":
              SCREAMING_SNAKE_CASE_      =     torch.tensor(
                  [
                      -4.0078e-01,
                      -3.8323e-04,
                      -1.2681e-01,
                      -1.1462e-01,
                      2.0095e-01,
                      1.0893e-01,
                      -8.8247e-02,
                      -3.0361e-01,
                      -9.8644e-03,
                  ]      )
        elif torch_device == "cpu":
              SCREAMING_SNAKE_CASE_      =     torch.tensor(
                  [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]      )
        else:
              SCREAMING_SNAKE_CASE_      =     torch.tensor(
                  [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]      )

        self.assertTrue(torch_all_close(__magic_name__     ,   __magic_name__     ,   rtol=1e-2      )      )



@slow
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				str     ,   __magic_name__       :				Optional[Any]     ,   __magic_name__       :				Union[str, Any]      )     ->		Union[str, Any]:
        return F'''gaussian_noise_s={seed}_shape={"_".join([str(__magic_name__      ) for s in shape]      )}.npy'''







  def       __A   (							self       :				Optional[int]      )     ->		Optional[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def       __A   (							self       :				int     ,   __magic_name__       :				Tuple=0     ,   __magic_name__       :				List[str]=(4, 3, 512, 512)     ,   __magic_name__       :				Any=False      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     torch.floataa if fpaa else torch.floataa
        SCREAMING_SNAKE_CASE_      =     torch.from_numpy(load_hf_numpy(self.get_file_format(__magic_name__     ,   __magic_name__      )      )      ).to(__magic_name__      ).to(__magic_name__      )
        return image







  def       __A   (							self       :				str     ,   __magic_name__       :				Dict="CompVis/stable-diffusion-v1-4"     ,   __magic_name__       :				Optional[Any]=False      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     "fp16" if fpaa else None
        SCREAMING_SNAKE_CASE_      =     torch.floataa if fpaa else torch.floataa

        SCREAMING_SNAKE_CASE_      =     AutoencoderKL.from_pretrained(
            __magic_name__     ,   subfolder="vae"     ,   torch_dtype=__magic_name__     ,   revision=__magic_name__     ,   )
        model.to(__magic_name__      ).eval()

        return model







  def       __A   (							self       :				int     ,   __magic_name__       :				List[str]=0      )     ->		Optional[Any]:
        if torch_device == "mps":
              return torch.manual_seed(__magic_name__      )
        return torch.Generator(device=__magic_name__      ).manual_seed(__magic_name__      )







  @parameterized.expand(
      [
          # fmt: off
          [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
          [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
          # fmt: on
      ]      )
  def       __A   (							self       :				Tuple     ,   __magic_name__       :				Optional[int]     ,   __magic_name__       :				Any     ,   __magic_name__       :				Dict      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     self.get_sd_vae_model()
        SCREAMING_SNAKE_CASE_      =     self.get_sd_image(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.get_generator(__magic_name__      )

        with torch.no_grad():
              SCREAMING_SNAKE_CASE_      =     model(__magic_name__     ,   generator=__magic_name__     ,   sample_posterior=__magic_name__      ).sample

        assert sample.shape == image.shape

        SCREAMING_SNAKE_CASE_      =     sample[-1, -2:, -2:, :2].flatten().float().cpu()
        SCREAMING_SNAKE_CASE_      =     torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice      )

        assert torch_all_close(__magic_name__     ,   __magic_name__     ,   atol=3e-3      )







  @parameterized.expand(
      [
          # fmt: off
          [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
          [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
          # fmt: on
      ]      )
  @require_torch_gpu
  def       __A   (							self       :				Any     ,   __magic_name__       :				int     ,   __magic_name__       :				Dict      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     self.get_sd_vae_model(fpaa=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.get_sd_image(__magic_name__     ,   fpaa=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.get_generator(__magic_name__      )

        with torch.no_grad():
              SCREAMING_SNAKE_CASE_      =     model(__magic_name__     ,   generator=__magic_name__     ,   sample_posterior=__magic_name__      ).sample

        assert sample.shape == image.shape

        SCREAMING_SNAKE_CASE_      =     sample[-1, -2:, :2, -2:].flatten().float().cpu()
        SCREAMING_SNAKE_CASE_      =     torch.tensor(__magic_name__      )

        assert torch_all_close(__magic_name__     ,   __magic_name__     ,   atol=1e-2      )







  @parameterized.expand(
      [
          # fmt: off
          [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
          [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
          # fmt: on
      ]      )
  def       __A   (							self       :				Any     ,   __magic_name__       :				Dict     ,   __magic_name__       :				Dict     ,   __magic_name__       :				Dict      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     self.get_sd_vae_model()
        SCREAMING_SNAKE_CASE_      =     self.get_sd_image(__magic_name__      )

        with torch.no_grad():
              SCREAMING_SNAKE_CASE_      =     model(__magic_name__      ).sample

        assert sample.shape == image.shape

        SCREAMING_SNAKE_CASE_      =     sample[-1, -2:, -2:, :2].flatten().float().cpu()
        SCREAMING_SNAKE_CASE_      =     torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice      )

        assert torch_all_close(__magic_name__     ,   __magic_name__     ,   atol=3e-3      )







  @parameterized.expand(
      [
          # fmt: off
          [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
          [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
          # fmt: on
      ]      )
  @require_torch_gpu
  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				int     ,   __magic_name__       :				str      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     self.get_sd_vae_model()
        SCREAMING_SNAKE_CASE_      =     self.get_sd_image(__magic_name__     ,   shape=(3, 4, 64, 64)      )

        with torch.no_grad():
              SCREAMING_SNAKE_CASE_      =     model.decode(__magic_name__      ).sample

        assert list(sample.shape      ) == [3, 3, 512, 512]

        SCREAMING_SNAKE_CASE_      =     sample[-1, -2:, :2, -2:].flatten().cpu()
        SCREAMING_SNAKE_CASE_      =     torch.tensor(__magic_name__      )

        assert torch_all_close(__magic_name__     ,   __magic_name__     ,   atol=1e-3      )







  @parameterized.expand(
      [
          # fmt: off
          [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
          [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
          # fmt: on
      ]      )
  @require_torch_gpu
  def       __A   (							self       :				str     ,   __magic_name__       :				Union[str, Any]     ,   __magic_name__       :				int      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     self.get_sd_vae_model(fpaa=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.get_sd_image(__magic_name__     ,   shape=(3, 4, 64, 64)     ,   fpaa=__magic_name__      )

        with torch.no_grad():
              SCREAMING_SNAKE_CASE_      =     model.decode(__magic_name__      ).sample

        assert list(sample.shape      ) == [3, 3, 512, 512]

        SCREAMING_SNAKE_CASE_      =     sample[-1, -2:, :2, -2:].flatten().float().cpu()
        SCREAMING_SNAKE_CASE_      =     torch.tensor(__magic_name__      )

        assert torch_all_close(__magic_name__     ,   __magic_name__     ,   atol=5e-3      )







  @parameterized.expand([(13,), (16,), (27,)]      )
  @require_torch_gpu
  @unittest.skipIf(not is_xformers_available()     ,   reason="xformers is not required when using PyTorch 2.0."      )
  def       __A   (							self       :				Tuple     ,   __magic_name__       :				str      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     self.get_sd_vae_model(fpaa=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.get_sd_image(__magic_name__     ,   shape=(3, 4, 64, 64)     ,   fpaa=__magic_name__      )

        with torch.no_grad():
              SCREAMING_SNAKE_CASE_      =     model.decode(__magic_name__      ).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_      =     model.decode(__magic_name__      ).sample

        assert list(sample.shape      ) == [3, 3, 512, 512]

        assert torch_all_close(__magic_name__     ,   __magic_name__     ,   atol=1e-1      )







  @parameterized.expand([(13,), (16,), (37,)]      )
  @require_torch_gpu
  @unittest.skipIf(not is_xformers_available()     ,   reason="xformers is not required when using PyTorch 2.0."      )
  def       __A   (							self       :				int     ,   __magic_name__       :				Union[str, Any]      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     self.get_sd_vae_model()
        SCREAMING_SNAKE_CASE_      =     self.get_sd_image(__magic_name__     ,   shape=(3, 4, 64, 64)      )

        with torch.no_grad():
              SCREAMING_SNAKE_CASE_      =     model.decode(__magic_name__      ).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_      =     model.decode(__magic_name__      ).sample

        assert list(sample.shape      ) == [3, 3, 512, 512]

        assert torch_all_close(__magic_name__     ,   __magic_name__     ,   atol=1e-2      )







  @parameterized.expand(
      [
          # fmt: off
          [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
          [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
          # fmt: on
      ]      )
  def       __A   (							self       :				Tuple     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				Optional[Any]      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     self.get_sd_vae_model()
        SCREAMING_SNAKE_CASE_      =     self.get_sd_image(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.get_generator(__magic_name__      )

        with torch.no_grad():
              SCREAMING_SNAKE_CASE_      =     model.encode(__magic_name__      ).latent_dist
              SCREAMING_SNAKE_CASE_      =     dist.sample(generator=__magic_name__      )

        assert list(sample.shape      ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

        SCREAMING_SNAKE_CASE_      =     sample[0, -1, -3:, -3:].flatten().cpu()
        SCREAMING_SNAKE_CASE_      =     torch.tensor(__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     3e-3 if torch_device != "mps" else 1e-2
        assert torch_all_close(__magic_name__     ,   __magic_name__     ,   atol=__magic_name__      )



import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin


@require_jieba
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  CpmAntTokenizer
  lowerCamelCase__   =  False







  def       __A   (							self       :				List[str]      )     ->		str:
        super().setUp()

        SCREAMING_SNAKE_CASE_      =     [
            "<d>",
            "</d>",
            "<s>",
            "</s>",
            "</_>",
            "<unk>",
            "<pad>",
            "</n>",
            "我",
            "是",
            "C",
            "P",
            "M",
            "A",
            "n",
            "t",
        ]
        SCREAMING_SNAKE_CASE_      =     os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]      )
        with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"      ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )







  @tooslow
  def       __A   (							self       :				int      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"      )
        SCREAMING_SNAKE_CASE_      =     "今天天气真好！"
        SCREAMING_SNAKE_CASE_      =     ["今天", "天气", "真", "好", "！"]
        SCREAMING_SNAKE_CASE_      =     tokenizer.tokenize(__magic_name__      )
        self.assertListEqual(__magic_name__     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     "今天天气真好！"
        SCREAMING_SNAKE_CASE_      =     [tokenizer.bos_token] + tokens

        SCREAMING_SNAKE_CASE_      =     [6, 9_802, 14_962, 2_082, 831, 244]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__      )     ,   __magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.decode(__magic_name__      )
        self.assertEqual(__magic_name__     ,   __magic_name__      )


