

"""simple docstring"""





import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard

   from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline




@require_flax
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			Optional[int]	):
    with tempfile.TemporaryDirectory() as tmpdirname:
       # pipeline has Flax weights
       __lowercase								=						FlaxDiffusionPipeline.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-pipe",					safety_checker=UpperCAmelCase__,					cache_dir=UpperCAmelCase__	)

       __lowercase								=						[t[-1] for t in os.walk(os.path.join(UpperCAmelCase__,					os.listdir(UpperCAmelCase__	)[0],					"snapshots"	)	)]
       __lowercase								=						[item for sublist in all_root_files for item in sublist]

       # None of the downloaded files should be a PyTorch file even if we have some here:
       # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
       assert not any(f.endswith(".bin"	) for f in files	)







@slow
@require_flax
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			List[Any]	):
    __lowercase		,__lowercase								=						FlaxStableDiffusionPipeline.from_pretrained(
        "hf-internal-testing/tiny-stable-diffusion-pipe",					safety_checker=UpperCAmelCase__	)

    __lowercase								=						(
        "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
        " field, close up, split lighting, cinematic"
    )

    __lowercase								=						jax.random.PRNGKey(0	)
    __lowercase								=						4

    __lowercase								=						jax.device_count()
    __lowercase								=						num_samples * [prompt]
    __lowercase								=						pipeline.prepare_inputs(UpperCAmelCase__	)

    # shard inputs and rng
    __lowercase								=						replicate(UpperCAmelCase__	)
    __lowercase								=						jax.random.split(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						shard(UpperCAmelCase__	)

    __lowercase								=						pipeline(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					jit=UpperCAmelCase__	).images

    assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
    if jax.device_count() == 8:
       assert np.abs(np.abs(images[0, 0, :2, :2, -2:],					dtype=np.floataa	).sum() - 4.1_514_745	) < 1E-3
       assert np.abs(np.abs(UpperCAmelCase__,					dtype=np.floataa	).sum() - 49_947.875	) < 5E-1

    __lowercase								=						pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]	)	)	)
    assert len(UpperCAmelCase__	) == num_samples


 def 					_lowercase (			self							:			int	):
    __lowercase		,__lowercase								=						FlaxStableDiffusionPipeline.from_pretrained(
        "CompVis/stable-diffusion-v1-4",					revision="flax",					safety_checker=UpperCAmelCase__	)

    __lowercase								=						(
        "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
        " field, close up, split lighting, cinematic"
    )

    __lowercase								=						jax.random.PRNGKey(0	)
    __lowercase								=						5_0

    __lowercase								=						jax.device_count()
    __lowercase								=						num_samples * [prompt]
    __lowercase								=						pipeline.prepare_inputs(UpperCAmelCase__	)

    # shard inputs and rng
    __lowercase								=						replicate(UpperCAmelCase__	)
    __lowercase								=						jax.random.split(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						shard(UpperCAmelCase__	)

    __lowercase								=						pipeline(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					jit=UpperCAmelCase__	).images

    assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
    if jax.device_count() == 8:
       assert np.abs((np.abs(images[0, 0, :2, :2, -2:],					dtype=np.floataa	).sum() - 0.05_652_401)	) < 1E-3
       assert np.abs((np.abs(UpperCAmelCase__,					dtype=np.floataa	).sum() - 2_383_808.2)	) < 5E-1


 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase		,__lowercase								=						FlaxStableDiffusionPipeline.from_pretrained(
        "CompVis/stable-diffusion-v1-4",					revision="bf16",					dtype=jnp.bfloataa,					safety_checker=UpperCAmelCase__	)

    __lowercase								=						(
        "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
        " field, close up, split lighting, cinematic"
    )

    __lowercase								=						jax.random.PRNGKey(0	)
    __lowercase								=						5_0

    __lowercase								=						jax.device_count()
    __lowercase								=						num_samples * [prompt]
    __lowercase								=						pipeline.prepare_inputs(UpperCAmelCase__	)

    # shard inputs and rng
    __lowercase								=						replicate(UpperCAmelCase__	)
    __lowercase								=						jax.random.split(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						shard(UpperCAmelCase__	)

    __lowercase								=						pipeline(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					jit=UpperCAmelCase__	).images

    assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
    if jax.device_count() == 8:
       assert np.abs((np.abs(images[0, 0, :2, :2, -2:],					dtype=np.floataa	).sum() - 0.04_003_906)	) < 1E-3
       assert np.abs((np.abs(UpperCAmelCase__,					dtype=np.floataa	).sum() - 2_373_516.75)	) < 5E-1


 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase		,__lowercase								=						FlaxStableDiffusionPipeline.from_pretrained(
        "CompVis/stable-diffusion-v1-4",					revision="bf16",					dtype=jnp.bfloataa	)

    __lowercase								=						(
        "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
        " field, close up, split lighting, cinematic"
    )

    __lowercase								=						jax.random.PRNGKey(0	)
    __lowercase								=						5_0

    __lowercase								=						jax.device_count()
    __lowercase								=						num_samples * [prompt]
    __lowercase								=						pipeline.prepare_inputs(UpperCAmelCase__	)

    # shard inputs and rng
    __lowercase								=						replicate(UpperCAmelCase__	)
    __lowercase								=						jax.random.split(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						shard(UpperCAmelCase__	)

    __lowercase								=						pipeline(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					jit=UpperCAmelCase__	).images

    assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
    if jax.device_count() == 8:
       assert np.abs((np.abs(images[0, 0, :2, :2, -2:],					dtype=np.floataa	).sum() - 0.04_003_906)	) < 1E-3
       assert np.abs((np.abs(UpperCAmelCase__,					dtype=np.floataa	).sum() - 2_373_516.75)	) < 5E-1


 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase								=						FlaxDDIMScheduler(
        beta_start=0.00_085,					beta_end=0.012,					beta_schedule="scaled_linear",					set_alpha_to_one=UpperCAmelCase__,					steps_offset=1,					)

    __lowercase		,__lowercase								=						FlaxStableDiffusionPipeline.from_pretrained(
        "CompVis/stable-diffusion-v1-4",					revision="bf16",					dtype=jnp.bfloataa,					scheduler=UpperCAmelCase__,					safety_checker=UpperCAmelCase__,					)
    __lowercase								=						scheduler.create_state()

    __lowercase								=						scheduler_state

    __lowercase								=						(
        "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
        " field, close up, split lighting, cinematic"
    )

    __lowercase								=						jax.random.PRNGKey(0	)
    __lowercase								=						5_0

    __lowercase								=						jax.device_count()
    __lowercase								=						num_samples * [prompt]
    __lowercase								=						pipeline.prepare_inputs(UpperCAmelCase__	)

    # shard inputs and rng
    __lowercase								=						replicate(UpperCAmelCase__	)
    __lowercase								=						jax.random.split(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						shard(UpperCAmelCase__	)

    __lowercase								=						pipeline(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					jit=UpperCAmelCase__	).images

    assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
    if jax.device_count() == 8:
       assert np.abs((np.abs(images[0, 0, :2, :2, -2:],					dtype=np.floataa	).sum() - 0.045_043_945)	) < 1E-3
       assert np.abs((np.abs(UpperCAmelCase__,					dtype=np.floataa	).sum() - 2_347_693.5)	) < 5E-1



 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						(
        "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
        " field, close up, split lighting, cinematic"
    )

    __lowercase								=						jax.device_count()
    __lowercase								=						num_samples * [prompt]
    __lowercase								=						jax.random.split(jax.random.PRNGKey(0	),					UpperCAmelCase__	)

    __lowercase		,__lowercase								=						FlaxStableDiffusionPipeline.from_pretrained(
        "CompVis/stable-diffusion-v1-4",					revision="bf16",					dtype=jnp.bfloataa,					safety_checker=UpperCAmelCase__,					)

    __lowercase								=						replicate(UpperCAmelCase__	)
    __lowercase								=						pipeline.prepare_inputs(UpperCAmelCase__	)
    __lowercase								=						shard(UpperCAmelCase__	)
    __lowercase								=						pipeline(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					jit=UpperCAmelCase__	).images
    assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
    __lowercase								=						images[2, 0, 2_5_6, 1_0:1_7, 1]

    # With memory efficient attention
    __lowercase		,__lowercase								=						FlaxStableDiffusionPipeline.from_pretrained(
        "CompVis/stable-diffusion-v1-4",					revision="bf16",					dtype=jnp.bfloataa,					safety_checker=UpperCAmelCase__,					use_memory_efficient_attention=UpperCAmelCase__,					)

    __lowercase								=						replicate(UpperCAmelCase__	)
    __lowercase								=						pipeline.prepare_inputs(UpperCAmelCase__	)
    __lowercase								=						shard(UpperCAmelCase__	)
    __lowercase								=						pipeline(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					jit=UpperCAmelCase__	).images
    assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
    __lowercase								=						images[2, 0, 2_5_6, 1_0:1_7, 1]

    # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
    # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
    assert abs(slice_eff - slice	).max() < 1E-2








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__		:					List[Any]												=  {
    """configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					int												=  [
          """MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MegatronBertForCausalLM""",
          """MegatronBertForMaskedLM""",
          """MegatronBertForMultipleChoice""",
          """MegatronBertForNextSentencePrediction""",
          """MegatronBertForPreTraining""",
          """MegatronBertForQuestionAnswering""",
          """MegatronBertForSequenceClassification""",
          """MegatronBertForTokenClassification""",
          """MegatronBertModel""",
          """MegatronBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_megatron_bert import (
                MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MegatronBertForCausalLM,
                MegatronBertForMaskedLM,
                MegatronBertForMultipleChoice,
                MegatronBertForNextSentencePrediction,
                MegatronBertForPreTraining,
                MegatronBertForQuestionAnswering,
                MegatronBertForSequenceClassification,
                MegatronBertForTokenClassification,
                MegatronBertModel,
                MegatronBertPreTrainedModel,
            )

else:
      import sys

      A__		:					List[Any]												=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






