
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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




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




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


								'''simple docstring'''




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

								__a										=    '''bird'''
								__a										=    jax.device_count()
								__a										=    pipe.prepare_text_inputs([prompts] * num_samples				)

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

								__a										=    jax.random.PRNGKey(0				)
								__a										=    jax.random.split(_snake_case		,     jax.device_count()				)

								__a										=    replicate(_snake_case				)
								__a										=    shard(_snake_case				)
								__a										=    shard(_snake_case				)

								__a										=    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)

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

								__a										=    jnp.asarray(jax.device_get(image_slice.flatten()				)				)
								__a										=    jnp.array(
								    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]				)
								print(F"""output_slice: {output_slice}"""				)
								assert jnp.abs(output_slice - expected_slice				).max() < 1E-2




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




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

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

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

								__a										=    jax.random.PRNGKey(0				)
								__a										=    jax.random.split(_snake_case		,     jax.device_count()				)

								__a										=    replicate(_snake_case				)
								__a										=    shard(_snake_case				)
								__a										=    shard(_snake_case				)

								__a										=    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)

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

								__a										=    jnp.asarray(jax.device_get(image_slice.flatten()				)				)
								__a										=    jnp.array(
								    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]				)
								print(F"""output_slice: {output_slice}"""				)
								assert jnp.abs(output_slice - expected_slice				).max() < 1E-2
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a  :     str       =  logging.get_logger(__name__)

__a  :     Optional[int]       =  {
    """google/vivit-b-16x2-kinetics400""": (
        """https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Tuple			 =						'''vivit'''





			def __init__(   self      ,   lowerCAmelCase__=2_24      ,   lowerCAmelCase__=32      ,   lowerCAmelCase__=[2, 16, 16]      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu_fast"      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-06      ,   lowerCAmelCase__=True      ,   **lowerCAmelCase__      ,   )     ->					int:

							'''simple docstring'''


							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							num_attention_heads
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_act
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							initializer_range
							__lowercase 			=							layer_norm_eps

							__lowercase 			=							image_size
							__lowercase 			=							num_frames
							__lowercase 			=							tubelet_size
							__lowercase 			=							num_channels
							__lowercase 			=							qkv_bias

							super().__init__(**lowerCAmelCase__						)