



"""simple docstring"""
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase     =							{"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =							["""DPTFeatureExtractor"""]
   _UpperCAmelCase     =							["""DPTImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =							[
       """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DPTForDepthEstimation""",
       """DPTForSemanticSegmentation""",
       """DPTModel""",
       """DPTPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_dpt import DPTFeatureExtractor
      from .image_processing_dpt import DPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dpt import (
          DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPTForDepthEstimation,
          DPTForSemanticSegmentation,
          DPTModel,
          DPTPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase     =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








"""simple docstring"""
import gc
import unittest

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


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard
@slow
@require_flax
class 					a	(  unittest.TestCase       ):



  def 	lowerCamelCase__       (						self :					Dict		)     ->					str:


     '''simple docstring'''




     super().tearDown()
     gc.collect()



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Dict     			=FlaxStableDiffusionPipeline.from_pretrained(
         """stabilityai/stable-diffusion-2"""       ,		revision="""bf16"""       ,		dtype=jnp.bfloataa       ,		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""A painting of a squirrel eating a burger"""
     SCREAMING_SNAKE_CASE_: Any     			=jax.device_count()
     SCREAMING_SNAKE_CASE_: Dict     			=num_samples * [prompt]
     SCREAMING_SNAKE_CASE_: List[Any]     			=sd_pipe.prepare_inputs(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=replicate(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=shard(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Dict     			=jax.random.PRNGKey(0		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=jax.random.split(lowerCAmelCase       ,		jax.device_count()		)

     SCREAMING_SNAKE_CASE_: Tuple     			=sd_pipe(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		num_inference_steps=25       ,		jit=lowerCAmelCase		)[0]
     assert images.shape == (jax.device_count(), 1, 768, 768, 3)

     SCREAMING_SNAKE_CASE_: Dict     			=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
     SCREAMING_SNAKE_CASE_: Dict     			=images[0, 253:256, 253:256, -1]

     SCREAMING_SNAKE_CASE_: Optional[int]     			=jnp.asarray(jax.device_get(image_slice.flatten()		)		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2]		)
     print(f'''output_slice: {output_slice}'''		)
     assert jnp.abs(output_slice - expected_slice		).max() < 1E-2





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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			="""stabilityai/stable-diffusion-2"""
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase       ,		subfolder="""scheduler"""		)
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=FlaxStableDiffusionPipeline.from_pretrained(
         lowerCAmelCase       ,		scheduler=lowerCAmelCase       ,		revision="""bf16"""       ,		dtype=jnp.bfloataa       ,		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=scheduler_params

     SCREAMING_SNAKE_CASE_: Tuple     			="""A painting of a squirrel eating a burger"""
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=jax.device_count()
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=num_samples * [prompt]
     SCREAMING_SNAKE_CASE_: List[Any]     			=sd_pipe.prepare_inputs(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Tuple     			=replicate(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=shard(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Any     			=jax.random.PRNGKey(0		)
     SCREAMING_SNAKE_CASE_: Any     			=jax.random.split(lowerCAmelCase       ,		jax.device_count()		)

     SCREAMING_SNAKE_CASE_: Tuple     			=sd_pipe(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		num_inference_steps=25       ,		jit=lowerCAmelCase		)[0]
     assert images.shape == (jax.device_count(), 1, 768, 768, 3)

     SCREAMING_SNAKE_CASE_: str     			=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
     SCREAMING_SNAKE_CASE_: Any     			=images[0, 253:256, 253:256, -1]

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=jnp.asarray(jax.device_get(image_slice.flatten()		)		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7]		)
     print(f'''output_slice: {output_slice}'''		)
     assert jnp.abs(output_slice - expected_slice		).max() < 1E-2



