



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 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





			def 		snake_case__			(				self	:       Optional[Any]   )						->					List[Any]:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       List[Any]   )						->					List[str]:
									"""simple docstring"""







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

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

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

									__lowercase					=				jax.random.PRNGKey(0   )
									__lowercase					=				jax.random.split(lowercase			,     jax.device_count()   )

									__lowercase					=				replicate(lowercase   )
									__lowercase					=				shard(lowercase   )
									__lowercase					=				shard(lowercase   )

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

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

									__lowercase					=				jnp.asarray(jax.device_get(image_slice.flatten()   )   )
									__lowercase					=				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 		snake_case__			(				self	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







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

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

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

									__lowercase					=				jax.random.PRNGKey(0   )
									__lowercase					=				jax.random.split(lowercase			,     jax.device_count()   )

									__lowercase					=				replicate(lowercase   )
									__lowercase					=				shard(lowercase   )
									__lowercase					=				shard(lowercase   )

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

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

									__lowercase					=				jnp.asarray(jax.device_get(image_slice.flatten()   )   )
									__lowercase					=				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










import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__ =						logging.get_logger(__name__)

UpperCamelCase__ =						{
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Union[str, Any]      =			"""sew"""





			def __init__(				self	:       List[Any]			,     lowercase	:       int=32			,     lowercase	:       List[str]=768			,     lowercase	:       Dict=12			,     lowercase	:       str=12			,     lowercase	:       str=3_072			,     lowercase	:       Optional[int]=2			,     lowercase	:       List[str]="gelu"			,     lowercase	:       List[str]=0.1			,     lowercase	:       Tuple=0.1			,     lowercase	:       Dict=0.1			,     lowercase	:       Any=0.0			,     lowercase	:       Dict=0.1			,     lowercase	:       Optional[int]=0.1			,     lowercase	:       List[str]=0.02			,     lowercase	:       Dict=1E-5			,     lowercase	:       Tuple="group"			,     lowercase	:       int="gelu"			,     lowercase	:       Any=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)			,     lowercase	:       Optional[int]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)			,     lowercase	:       Any=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)			,     lowercase	:       List[str]=False			,     lowercase	:       Tuple=128			,     lowercase	:       int=16			,     lowercase	:       Union[str, Any]=True			,     lowercase	:       List[str]=0.05			,     lowercase	:       Optional[int]=10			,     lowercase	:       Any=2			,     lowercase	:       Optional[Any]=0.0			,     lowercase	:       Optional[Any]=10			,     lowercase	:       int=0			,     lowercase	:       Optional[int]="mean"			,     lowercase	:       List[Any]=False			,     lowercase	:       str=False			,     lowercase	:       int=256			,     lowercase	:       str=0			,     lowercase	:       List[Any]=1			,     lowercase	:       List[Any]=2			,     **lowercase	:       List[Any]			,     )						->					Optional[Any]:
									"""simple docstring"""







									super().__init__(**lowercase			,     pad_token_id=lowercase			,     bos_token_id=lowercase			,     eos_token_id=lowercase   )
									__lowercase					=				hidden_size
									__lowercase					=				feat_extract_norm
									__lowercase					=				feat_extract_activation
									__lowercase					=				list(lowercase   )
									__lowercase					=				list(lowercase   )
									__lowercase					=				list(lowercase   )
									__lowercase					=				conv_bias
									__lowercase					=				num_conv_pos_embeddings
									__lowercase					=				num_conv_pos_embedding_groups
									__lowercase					=				len(self.conv_dim   )
									__lowercase					=				num_hidden_layers
									__lowercase					=				intermediate_size
									__lowercase					=				squeeze_factor
									__lowercase					=				hidden_act
									__lowercase					=				num_attention_heads
									__lowercase					=				hidden_dropout
									__lowercase					=				attention_dropout
									__lowercase					=				activation_dropout
									__lowercase					=				feat_proj_dropout
									__lowercase					=				final_dropout
									__lowercase					=				layerdrop
									__lowercase					=				layer_norm_eps
									__lowercase					=				initializer_range
									__lowercase					=				vocab_size

									if (
									    (len(self.conv_stride   ) != self.num_feat_extract_layers)
									    or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
									    or (len(self.conv_dim   ) != self.num_feat_extract_layers)
									):
															raise ValueError(
															    """Configuration for convolutional layers is incorrect."""
															    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
															    F"but is `len(config.conv_dim) = {len(self.conv_dim   )}`, `len(config.conv_stride)"
															    F"= {len(self.conv_stride   )}`, `len(config.conv_kernel) = {len(self.conv_kernel   )}`."   )

									# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
									__lowercase					=				apply_spec_augment
									__lowercase					=				mask_time_prob
									__lowercase					=				mask_time_length
									__lowercase					=				mask_time_min_masks
									__lowercase					=				mask_feature_prob
									__lowercase					=				mask_feature_length
									__lowercase					=				mask_feature_min_masks

									# ctc loss
									__lowercase					=				ctc_loss_reduction
									__lowercase					=				ctc_zero_infinity

									# sequence classification
									__lowercase					=				use_weighted_layer_sum
									__lowercase					=				classifier_proj_size







			@property
			def 		snake_case__			(				self	:       Dict   )						->					str:
									"""simple docstring"""







									return functools.reduce(operator.mul			,     self.conv_stride			,     1   )





