


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     __UpperCAmelCase							(       __A ,					__A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      AutoencoderKL
     _lowerCamelCase   =      """sample"""
     _lowerCamelCase   =      1e-2


     @property
     def 	snake_case_  (	self    ):
        __a					=  4
        __a					=  3
        __a					=  (32, 32)

        __a					=  floats_tensor((batch_size, num_channels) + sizes    ).to(__A    )

        return {"sample": image}


     @property
     def 	snake_case_  (	self    ):
        return (3, 32, 32)


     @property
     def 	snake_case_  (	self    ):
        return (3, 32, 32)


     def 	snake_case_  (	self    ):
        __a					=  {
            """block_out_channels""": [32, 64],
            """in_channels""": 3,
            """out_channels""": 3,
            """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
            """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
            """latent_channels""": 4,
        }
        __a					=  self.dummy_input
        return init_dict, inputs_dict


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        pass


     @unittest.skipIf(torch_device == """mps"""  ,       """Gradient checkpointing skipped on MPS"""    )
     def 	snake_case_  (	self    ):
        # enable deterministic behavior for gradient checkpointing
        __a	,    __a					=  self.prepare_init_args_and_inputs_for_common()
        __a					=  self.model_class(**__A    )
        model.to(__A    )

        assert not model.is_gradient_checkpointing and model.training

        __a					=  model(**__A    ).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()

        __a					=  torch.randn_like(__A    )
        __a					=  (out - labels).mean()
        loss.backward()

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

        assert model_a.is_gradient_checkpointing and model_a.training

        __a					=  model_a(**__A    ).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()
        __a					=  (out_a - labels).mean()
        loss_a.backward()

        # compare the output and parameters gradients
        self.assertTrue((loss - loss_a).abs() < 1E-5    )
        __a					=  dict(model.named_parameters()    )
        __a					=  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 	snake_case_  (	self    ):
        __a	,    __a					=  AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""  ,       output_loading_info=__A    )
        self.assertIsNotNone(__A    )
        self.assertEqual(len(loading_info["""missing_keys"""]    )  ,       0    )

        model.to(__A    )
        __a					=  model(**self.dummy_input    )

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


     def 	snake_case_  (	self    ):
        __a					=  AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""    )
        __a					=  model.to(__A    )
        model.eval()

        if torch_device == "mps":
           __a					=  torch.manual_seed(0    )
        else:
           __a					=  torch.Generator(device=__A    ).manual_seed(0    )

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

        __a					=  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":
           __a					=  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":
           __a					=  torch.tensor(
               [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]    )
        else:
           __a					=  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(__A  ,       __A  ,       rtol=1E-2    )    )







@slow
class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        return f'''gaussian_noise_s={seed}_shape={'_'.join([str(__A    ) for s in shape]    )}.npy'''


     def 	snake_case_  (	self    ):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


     def 	snake_case_  (	self  ,       __A=0  ,       __A=(4, 3, 512, 512)  ,       __A=False    ):
        __a					=  torch.floataa if fpaa else torch.floataa
        __a					=  torch.from_numpy(load_hf_numpy(self.get_file_format(__A  ,       __A    )    )    ).to(__A    ).to(__A    )
        return image


     def 	snake_case_  (	self  ,       __A="CompVis/stable-diffusion-v1-4"  ,       __A=False    ):
        __a					=  """fp16""" if fpaa else None
        __a					=  torch.floataa if fpaa else torch.floataa

        __a					=  AutoencoderKL.from_pretrained(
            __A  ,       subfolder="""vae"""  ,       torch_dtype=__A  ,       revision=__A  ,       )
        model.to(__A    ).eval()

        return model


     def 	snake_case_  (	self  ,       __A=0    ):
        if torch_device == "mps":
           return torch.manual_seed(__A    )
        return torch.Generator(device=__A    ).manual_seed(__A    )


     @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 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        __a					=  self.get_sd_vae_model()
        __a					=  self.get_sd_image(__A    )
        __a					=  self.get_generator(__A    )

        with torch.no_grad():
           __a					=  model(__A  ,       generator=__A  ,       sample_posterior=__A    ).sample

        assert sample.shape == image.shape

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

        assert torch_all_close(__A  ,       __A  ,       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 	snake_case_  (	self  ,       __A  ,       __A    ):
        __a					=  self.get_sd_vae_model(fpaa=__A    )
        __a					=  self.get_sd_image(__A  ,       fpaa=__A    )
        __a					=  self.get_generator(__A    )

        with torch.no_grad():
           __a					=  model(__A  ,       generator=__A  ,       sample_posterior=__A    ).sample

        assert sample.shape == image.shape

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

        assert torch_all_close(__A  ,       __A  ,       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 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        __a					=  self.get_sd_vae_model()
        __a					=  self.get_sd_image(__A    )

        with torch.no_grad():
           __a					=  model(__A    ).sample

        assert sample.shape == image.shape

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

        assert torch_all_close(__A  ,       __A  ,       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 	snake_case_  (	self  ,       __A  ,       __A    ):
        __a					=  self.get_sd_vae_model()
        __a					=  self.get_sd_image(__A  ,       shape=(3, 4, 64, 64)    )

        with torch.no_grad():
           __a					=  model.decode(__A    ).sample

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

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

        assert torch_all_close(__A  ,       __A  ,       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 	snake_case_  (	self  ,       __A  ,       __A    ):
        __a					=  self.get_sd_vae_model(fpaa=__A    )
        __a					=  self.get_sd_image(__A  ,       shape=(3, 4, 64, 64)  ,       fpaa=__A    )

        with torch.no_grad():
           __a					=  model.decode(__A    ).sample

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

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

        assert torch_all_close(__A  ,       __A  ,       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 	snake_case_  (	self  ,       __A    ):
        __a					=  self.get_sd_vae_model(fpaa=__A    )
        __a					=  self.get_sd_image(__A  ,       shape=(3, 4, 64, 64)  ,       fpaa=__A    )

        with torch.no_grad():
           __a					=  model.decode(__A    ).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
           __a					=  model.decode(__A    ).sample

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

        assert torch_all_close(__A  ,       __A  ,       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 	snake_case_  (	self  ,       __A    ):
        __a					=  self.get_sd_vae_model()
        __a					=  self.get_sd_image(__A  ,       shape=(3, 4, 64, 64)    )

        with torch.no_grad():
           __a					=  model.decode(__A    ).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
           __a					=  model.decode(__A    ).sample

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

        assert torch_all_close(__A  ,       __A  ,       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 	snake_case_  (	self  ,       __A  ,       __A    ):
        __a					=  self.get_sd_vae_model()
        __a					=  self.get_sd_image(__A    )
        __a					=  self.get_generator(__A    )

        with torch.no_grad():
           __a					=  model.encode(__A    ).latent_dist
           __a					=  dist.sample(generator=__A    )

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

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

        __a					=  3E-3 if torch_device != """mps""" else 1E-2
        assert torch_all_close(__A  ,       __A  ,       atol=__A    )









'''simple docstring'''


import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
				import optax
				from flax.training.common_utils import onehot

				from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
				from transformers.models.ta.modeling_flax_ta import shift_tokens_right

@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      A       (						unittest.TestCase       ):







							@slow
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
														_a      	=							AutoTokenizer.from_pretrained('''google/mt5-small'''   )

														_a      	=							tokenizer('''Hello there'''				,     return_tensors='''np'''   ).input_ids
														_a      	=							tokenizer('''Hi I am'''				,     return_tensors='''np'''   ).input_ids

														_a      	=							shift_tokens_right(lowerCAmelCase_				,     model.config.pad_token_id				,     model.config.decoder_start_token_id   )

														_a      	=							model(lowerCAmelCase_				,     decoder_input_ids=lowerCAmelCase_   ).logits
														_a      	=							optax.softmax_cross_entropy(lowerCAmelCase_				,     onehot(lowerCAmelCase_				,     logits.shape[-1]   )   ).mean()

														_a      	=							-(labels.shape[-1] * loss.item())

														_a      	=							-8_4.9_1_2_7
														self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )

