
'''simple docstring'''
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class       _lowerCAmelCase   (   __snake_case					,							__snake_case				):







       '''simple docstring'''

       @register_to_config
       def __init__(self  ,  *,
           UpperCAmelCase = 4  ,  UpperCAmelCase = 768  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  )					->       int:
              super().__init__()

              _snake_case       =  nn.Parameter(torch.zeros(lowercase_  )  )

              # parameters for additional clip time embeddings
              _snake_case       =  nn.Linear(lowercase_  ,  lowercase_  )
              _snake_case       =  nn.Linear(lowercase_  ,  lowercase_  )

              # parameters for encoder hidden states
              _snake_case       =  clip_extra_context_tokens
              _snake_case       =  nn.Linear(
                  lowercase_  ,  self.clip_extra_context_tokens * cross_attention_dim  )
              _snake_case       =  nn.Linear(lowercase_  ,  lowercase_  )
              _snake_case       =  nn.LayerNorm(lowercase_  )





       def 	lowercase							(self  ,  *, UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[Any]:







              if do_classifier_free_guidance:
                     # Add the classifier free guidance embeddings to the image embeddings
                     _snake_case       =  image_embeddings.shape[0]
                     _snake_case       =  self.learned_classifier_free_guidance_embeddings.unsqueeze(0  )
                     _snake_case       =  classifier_free_guidance_embeddings.expand(
                         lowercase_  ,  -1  )
                     _snake_case       =  torch.cat([classifier_free_guidance_embeddings, image_embeddings]  ,  dim=0  )

              # The image embeddings batch size and the text embeddings batch size are equal
              assert image_embeddings.shape[0] == prompt_embeds.shape[0]

              _snake_case       =  prompt_embeds.shape[0]

              # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
              # adding CLIP embeddings to the existing timestep embedding, ...
              _snake_case       =  self.embedding_proj(lowercase_  )
              _snake_case       =  self.clip_image_embeddings_project_to_time_embeddings(lowercase_  )
              _snake_case       =  time_projected_image_embeddings + time_projected_prompt_embeds

              # ... and by projecting CLIP embeddings into four
              # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
              _snake_case       =  self.clip_extra_context_tokens_proj(lowercase_  )
              _snake_case       =  clip_extra_context_tokens.reshape(lowercase_  ,  -1  ,  self.clip_extra_context_tokens  )
              _snake_case       =  clip_extra_context_tokens.permute(0  ,  2  ,  1  )

              _snake_case       =  self.encoder_hidden_states_proj(lowercase_  )
              _snake_case       =  self.text_encoder_hidden_states_norm(lowercase_  )
              _snake_case       =  torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]  ,  dim=1  )

              return text_encoder_hidden_states, additive_clip_time_embeddings

'''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       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       Optional[int]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()

							def 	lowercase							(self  )					->       Dict:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-canny"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

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

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

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

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

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

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

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  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 	lowercase							(self  )					->       Optional[int]:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-openpose"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

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

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

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

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

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

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

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