






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__   :						Dict													=   {
    """configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						str													=   [
     """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

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








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   UpperCamelCase			(  unittest.TestCase ):

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmpdirname:
      # pipeline has Flax weights
      SCREAMING_SNAKE_CASE		    =      FlaxDiffusionPipeline.from_pretrained(
          'hf-internal-testing/tiny-stable-diffusion-pipe'				,     safety_checker=snake_case__				,     cache_dir=snake_case__     )

      SCREAMING_SNAKE_CASE		    =      [t[-1] for t in os.walk(os.path.join(snake_case__				,     os.listdir(snake_case__     )[0]				,     'snapshots'     )     )]
      SCREAMING_SNAKE_CASE		    =      [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   UpperCamelCase			(  unittest.TestCase ):

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'hf-internal-testing/tiny-stable-diffusion-pipe'				,     safety_checker=snake_case__     )

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

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

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).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(snake_case__				,     dtype=np.floataa     ).sum() - 49_947.875     ) < 5E-1

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

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='flax'				,     safety_checker=snake_case__     )

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

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

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).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(snake_case__				,     dtype=np.floataa     ).sum() - 2_383_808.2)     ) < 5E-1

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='bf16'				,     dtype=jnp.bfloataa				,     safety_checker=snake_case__     )

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

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

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).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(snake_case__				,     dtype=np.floataa     ).sum() - 2_373_516.75)     ) < 5E-1

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='bf16'				,     dtype=jnp.bfloataa     )

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

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

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).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(snake_case__				,     dtype=np.floataa     ).sum() - 2_373_516.75)     ) < 5E-1

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      FlaxDDIMScheduler(
         beta_start=0.00_085				,     beta_end=0.012				,     beta_schedule='scaled_linear'				,     set_alpha_to_one=snake_case__				,     steps_offset=1				,     )

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='bf16'				,     dtype=jnp.bfloataa				,     scheduler=snake_case__				,     safety_checker=snake_case__				,     )
     SCREAMING_SNAKE_CASE		    =      scheduler.create_state()

     SCREAMING_SNAKE_CASE		    =      scheduler_state

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

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

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).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(snake_case__				,     dtype=np.floataa     ).sum() - 2_347_693.5)     ) < 5E-1




    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='bf16'				,     dtype=jnp.bfloataa				,     safety_checker=snake_case__				,     )

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

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

     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images
     assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     SCREAMING_SNAKE_CASE		    =      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

