


import torch

from transformers import AutoModel







class 	snake_case_       (torch.nn.Module				):
					def __init__(				self       :List[Any]      ,__snake_case       :List[str]="sayef/fsner-bert-base-uncased"				) ->   str:
												super(__snake_case      ,self				).__init__()

												a__					=					AutoModel.from_pretrained(__snake_case      ,return_dict=__snake_case				)
												a__					=					torch.nn.CosineSimilarity(3      ,1E-08				)
												a__					=					torch.nn.Softmax(dim=1				)
					def 			lowerCamelCase__(				self       :Dict      ,**__snake_case       :Any				) ->   List[Any]:
												return self.bert(**__snake_case				).last_hidden_state
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :List[str]				) ->   Any:
												return token_embeddings.sum(2      ,keepdim=__snake_case				)
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :Optional[int]      ,__snake_case       :List[Any]      ,__snake_case       :str=1				) ->   Optional[Any]:
												return self.softmax(T * self.cos(__snake_case      ,__snake_case				)				)



					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :Union[str, Any]      ,__snake_case       :Union[str, Any]				) ->   Any:
												a__					=					W_supports['sizes'].tolist()
												a__					=					W_supports['start_token_id'].item()
												a__					=					W_supports['end_token_id'].item()

												del W_supports["sizes"]
												del W_supports["start_token_id"]
												del W_supports["end_token_id"]

												a__					=					self.BERT(**__snake_case				)
												a__					=					self.BERT(**__snake_case				)

												a__					=					None
												a__					=					None

												a__					=					W_supports['input_ids'] == start_token_id
												a__					=					W_supports['input_ids'] == end_token_id

												for i, size in enumerate(__snake_case				):
																			if i == 0:
																										a__					=					0
																			else:
																										a__					=					support_sizes[i - 1]

																			a__					=					S[s : s + size][start_token_masks[s : s + size]]
																			a__					=					S[s : s + size][end_token_masks[s : s + size]]

																			a__					=					torch.matmul(q[i]      ,s_start.T				).sum(1				).softmax(0				)
																			a__					=					torch.matmul(q[i]      ,s_end.T				).sum(1				).softmax(0				)

																			if p_starts is not None:
																										a__					=					torch.vstack((p_starts, p_start)				)
																										a__					=					torch.vstack((p_ends, p_end)				)
																			else:
																										a__					=					p_start
																										a__					=					p_end

												return p_starts, p_ends








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[Any]				) ->   str:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
					def 			lowerCamelCase__(				self       :int				) ->   Optional[Any]:
												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, 7_68, 5_12, 3)

												a__					=					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
												a__					=					images[0, 2_53:2_56, 2_53:2_56, -1]

												a__					=					jnp.asarray(jax.device_get(image_slice.flatten()				)				)
												a__					=					jnp.array(
												    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]				)
												print(F'output_slice: {output_slice}'				)
												assert jnp.abs(output_slice - expected_slice				).max() < 1E-2



					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Optional[Any]:
												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, 7_68, 5_12, 3)

												a__					=					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
												a__					=					images[0, 2_53:2_56, 2_53:2_56, -1]

												a__					=					jnp.asarray(jax.device_get(image_slice.flatten()				)				)
												a__					=					jnp.array(
												    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]				)
												print(F'output_slice: {output_slice}'				)
												assert jnp.abs(output_slice - expected_slice				).max() < 1E-2




