




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 __snake_case			(			unittest.TestCase		):







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Union[str, 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 lowerCamelCase						(      self		:						Dict):

        """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












from typing import TYPE_CHECKING

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


snake_case_ : Union[str, Any]    		=       {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Dict    		=       ["GPTNeoXTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       [
        "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
        "GPTNeoXForCausalLM",
        "GPTNeoXForQuestionAnswering",
        "GPTNeoXForSequenceClassification",
        "GPTNeoXForTokenClassification",
        "GPTNeoXLayer",
        "GPTNeoXModel",
        "GPTNeoXPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_neox import (
            GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
            GPTNeoXLayer,
            GPTNeoXModel,
            GPTNeoXPreTrainedModel,
        )


else:
    import sys

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






