



from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
     from ..ta.tokenization_ta import TaTokenizer
else:
     from ...utils.dummy_sentencepiece_objects import TaTokenizer

a_						:     List[str]	     =		TaTokenizer

if is_tokenizers_available():
     from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
     from ...utils.dummy_tokenizers_objects import TaTokenizerFast

a_						:     List[Any]	     =		TaTokenizerFast

a_						:     str	     =		{'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     Union[str, Any]	     =		[
         'MT5EncoderModel',
         'MT5ForConditionalGeneration',
         'MT5ForQuestionAnswering',
         'MT5Model',
         'MT5PreTrainedModel',
         'MT5Stack',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     Union[str, Any]	     =		['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     Tuple	     =		['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
     from .configuration_mta import MTaConfig, MTaOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mta import (
              MTaEncoderModel,
              MTaForConditionalGeneration,
              MTaForQuestionAnswering,
              MTaModel,
              MTaPreTrainedModel,
              MTaStack,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
     import sys

     a_						:     int	     =		_LazyModule(
         __name__,
         globals()['__file__'],
         _import_structure,
         extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
         module_spec=__spec__,
     )




import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, 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    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



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







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/sd2-inpaint/init_image.png"    )
           lowerCamelCase								=		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"    )

           lowerCamelCase								=		"xvjiarui/stable-diffusion-2-inpainting"
           lowerCamelCase			,							lowerCamelCase								=		FlaxStableDiffusionInpaintPipeline.from_pretrained(__a				,			safety_checker=__a    )

           lowerCamelCase								=		"Face of a yellow cat, high resolution, sitting on a park bench"

           lowerCamelCase								=		jax.random.PRNGKey(0    )
           lowerCamelCase								=		50

           lowerCamelCase								=		jax.device_count()
           lowerCamelCase								=		num_samples * [prompt]
           lowerCamelCase								=		num_samples * [init_image]
           lowerCamelCase								=		num_samples * [mask_image]
           lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		pipeline.prepare_inputs(__a				,			__a				,			__a    )

           # shard inputs and rng
           lowerCamelCase								=		replicate(__a    )
           lowerCamelCase								=		jax.random.split(__a				,			jax.device_count()    )
           lowerCamelCase								=		shard(__a    )
           lowerCamelCase								=		shard(__a    )
           lowerCamelCase								=		shard(__a    )

           lowerCamelCase								=		pipeline(
               __a				,			__a				,			__a				,			__a				,			__a				,			__a				,			jit=__a    )

           lowerCamelCase								=		output.images.reshape(__a				,			5_12				,			5_12				,			3    )

           lowerCamelCase								=		images[0, 2_53:2_56, 2_53:2_56, -1]

           lowerCamelCase								=		jnp.asarray(jax.device_get(image_slice.flatten()    )    )
           lowerCamelCase								=		jnp.array(
               [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]    )
           print(F"""output_slice: {output_slice}"""    )
           assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2