


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    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						int			  =						FlaxStableDiffusionPipeline.from_pretrained(
             """stabilityai/stable-diffusion-2"""							,    revision="""bf16"""							,    dtype=jnp.bfloataa							,    )

         __snake_case:						int			  =						"""A painting of a squirrel eating a burger"""
         __snake_case:						str			  =						jax.device_count()
         __snake_case:						Optional[int]			  =						num_samples * [prompt]
         __snake_case:						Optional[int]			  =						sd_pipe.prepare_inputs(A		)

         __snake_case:						Any			  =						replicate(A		)
         __snake_case:						Optional[int]			  =						shard(A		)

         __snake_case:						List[Any]			  =						jax.random.PRNGKey(0		)
         __snake_case:						int			  =						jax.random.split(A							,    jax.device_count()		)

         __snake_case:						List[Any]			  =						sd_pipe(A							,    A							,    A							,    num_inference_steps=25							,    jit=A		)[0]
         assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

         __snake_case:						Dict			  =						jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         __snake_case:						List[Any]			  =						jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.4_5508, 0.4512]		)
         print(f'''output_slice: {output_slice}'''		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1E-2






       def 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						List[Any]			  =						"""stabilityai/stable-diffusion-2"""
         __snake_case:						int			  =						FlaxDPMSolverMultistepScheduler.from_pretrained(A							,    subfolder="""scheduler"""		)
         __snake_case:						Optional[Any]			  =						FlaxStableDiffusionPipeline.from_pretrained(
             A							,    scheduler=A							,    revision="""bf16"""							,    dtype=jnp.bfloataa							,    )
         __snake_case:						Tuple			  =						scheduler_params

         __snake_case:						Optional[int]			  =						"""A painting of a squirrel eating a burger"""
         __snake_case:						str			  =						jax.device_count()
         __snake_case:						Dict			  =						num_samples * [prompt]
         __snake_case:						Tuple			  =						sd_pipe.prepare_inputs(A		)

         __snake_case:						Tuple			  =						replicate(A		)
         __snake_case:						List[Any]			  =						shard(A		)

         __snake_case:						Any			  =						jax.random.PRNGKey(0		)
         __snake_case:						Dict			  =						jax.random.split(A							,    jax.device_count()		)

         __snake_case:						List[Any]			  =						sd_pipe(A							,    A							,    A							,    num_inference_steps=25							,    jit=A		)[0]
         assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

         __snake_case:						Optional[int]			  =						jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         __snake_case:						List[str]			  =						jnp.array([0.4336, 0.4_2969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]		)
         print(f'''output_slice: {output_slice}'''		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1E-2













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase	:       List[str]    	=							{
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[Any]    	=							["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Tuple    	=							[
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[int]    	=							[
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       List[Any]    	=							[
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase	:       str    	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






