

'''simple docstring'''

import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
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     a_						(      unittest.TestCase    ):

  def   A__						(				self				)  ->				str:



        """simple docstring"""






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

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase  ,UpperCamelCase      = FlaxControlNetModel.from_pretrained(
            """lllyasviel/sd-controlnet-canny"""      ,		from_pt=__snake_case      ,		dtype=jnp.bfloataa				)
        UpperCamelCase  ,UpperCamelCase      = FlaxStableDiffusionControlNetPipeline.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""      ,		controlnet=__snake_case      ,		from_pt=__snake_case      ,		dtype=jnp.bfloataa				)
        UpperCamelCase      = controlnet_params

        UpperCamelCase      = """bird"""
        UpperCamelCase      = jax.device_count()
        UpperCamelCase      = pipe.prepare_text_inputs([prompts] * num_samples				)

        UpperCamelCase      = load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""				)
        UpperCamelCase      = pipe.prepare_image_inputs([canny_image] * num_samples				)

        UpperCamelCase      = jax.random.PRNGKey(0				)
        UpperCamelCase      = jax.random.split(__snake_case      ,		jax.device_count()				)

        UpperCamelCase      = replicate(__snake_case				)
        UpperCamelCase      = shard(__snake_case				)
        UpperCamelCase      = shard(__snake_case				)

        UpperCamelCase      = pipe(
            prompt_ids=__snake_case      ,		image=__snake_case      ,		params=__snake_case      ,		prng_seed=__snake_case      ,		num_inference_steps=50      ,		jit=__snake_case      ,		).images
        assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

        UpperCamelCase      = jnp.asarray(jax.device_get(image_slice.flatten()				)				)
        UpperCamelCase      = jnp.array(
            [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]				)
        print(F"output_slice: {output_slice}"				)
        assert jnp.abs(output_slice - expected_slice				).max() < 1e-2


  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase  ,UpperCamelCase      = FlaxControlNetModel.from_pretrained(
            """lllyasviel/sd-controlnet-openpose"""      ,		from_pt=__snake_case      ,		dtype=jnp.bfloataa				)
        UpperCamelCase  ,UpperCamelCase      = FlaxStableDiffusionControlNetPipeline.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""      ,		controlnet=__snake_case      ,		from_pt=__snake_case      ,		dtype=jnp.bfloataa				)
        UpperCamelCase      = controlnet_params

        UpperCamelCase      = """Chef in the kitchen"""
        UpperCamelCase      = jax.device_count()
        UpperCamelCase      = pipe.prepare_text_inputs([prompts] * num_samples				)

        UpperCamelCase      = load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""				)
        UpperCamelCase      = pipe.prepare_image_inputs([pose_image] * num_samples				)

        UpperCamelCase      = jax.random.PRNGKey(0				)
        UpperCamelCase      = jax.random.split(__snake_case      ,		jax.device_count()				)

        UpperCamelCase      = replicate(__snake_case				)
        UpperCamelCase      = shard(__snake_case				)
        UpperCamelCase      = shard(__snake_case				)

        UpperCamelCase      = pipe(
            prompt_ids=__snake_case      ,		image=__snake_case      ,		params=__snake_case      ,		prng_seed=__snake_case      ,		num_inference_steps=50      ,		jit=__snake_case      ,		).images
        assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

        UpperCamelCase      = jnp.asarray(jax.device_get(image_slice.flatten()				)				)
        UpperCamelCase      = jnp.array(
            [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]				)
        print(F"output_slice: {output_slice}"				)
        assert jnp.abs(output_slice - expected_slice				).max() < 1e-2








'''simple docstring'''

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__     =       {
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       ['MobileBertTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MobileBertForMaskedLM',
         'MobileBertForMultipleChoice',
         'MobileBertForNextSentencePrediction',
         'MobileBertForPreTraining',
         'MobileBertForQuestionAnswering',
         'MobileBertForSequenceClassification',
         'MobileBertForTokenClassification',
         'MobileBertLayer',
         'MobileBertModel',
         'MobileBertPreTrainedModel',
         'load_tf_weights_in_mobilebert',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMobileBertForMaskedLM',
         'TFMobileBertForMultipleChoice',
         'TFMobileBertForNextSentencePrediction',
         'TFMobileBertForPreTraining',
         'TFMobileBertForQuestionAnswering',
         'TFMobileBertForSequenceClassification',
         'TFMobileBertForTokenClassification',
         'TFMobileBertMainLayer',
         'TFMobileBertModel',
         'TFMobileBertPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mobilebert import (
         MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileBertConfig,
         MobileBertOnnxConfig,
     )
     from .tokenization_mobilebert import MobileBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mobilebert_fast import MobileBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilebert import (
              MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
              MobileBertLayer,
              MobileBertModel,
              MobileBertPreTrainedModel,
              load_tf_weights_in_mobilebert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilebert import (
              TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileBertForMaskedLM,
              TFMobileBertForMultipleChoice,
              TFMobileBertForNextSentencePrediction,
              TFMobileBertForPreTraining,
              TFMobileBertForQuestionAnswering,
              TFMobileBertForSequenceClassification,
              TFMobileBertForTokenClassification,
              TFMobileBertMainLayer,
              TFMobileBertModel,
              TFMobileBertPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE__     =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
