






import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


SCREAMING_SNAKE_CASE__				     =			{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class 			_UpperCamelCase(       nn.Module		):

		def __init__(					self		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ):



					'''simple docstring'''



					super().__init__()
					__a						:  str									=     torchvision.models.resnetaaa(pretrained=SCREAMING_SNAKE_CASE__    )
					__a						:  Any									=     list(model.children()    )[:-2]
					__a						:  Dict									=     nn.Sequential(*SCREAMING_SNAKE_CASE__    )
					__a						:  List[str]									=     nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]    )



		def    __lowerCAmelCase  (					self		:    int    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  List[str]									=     self.pool(self.model(SCREAMING_SNAKE_CASE__    )    )
					__a						:  Optional[int]									=     torch.flatten(SCREAMING_SNAKE_CASE__    ,       start_dim=2    )
					__a						:  Optional[Any]									=     out.transpose(1    ,       2    ).contiguous()
					return out  # BxNx2048






class 			_UpperCamelCase(       __lowerCamelCase		):

		def __init__(					self		:    int    ,       SCREAMING_SNAKE_CASE__		:    Any    ,       SCREAMING_SNAKE_CASE__		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ):



					'''simple docstring'''



					__a						:  str									=     [json.loads(SCREAMING_SNAKE_CASE__    ) for l in open(SCREAMING_SNAKE_CASE__    )]
					__a						:  List[str]									=     os.path.dirname(SCREAMING_SNAKE_CASE__    )
					__a						:  str									=     tokenizer
					__a						:  int									=     labels
					__a						:  Dict									=     len(SCREAMING_SNAKE_CASE__    )
					__a						:  Tuple									=     max_seq_length

					__a						:  int									=     transforms

		def __len__(					self		:    List[Any]    ):



					'''simple docstring'''



					return len(self.data    )

		def __getitem__(					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     torch.LongTensor(self.tokenizer.encode(self.data[index]['text']    ,       add_special_tokens=SCREAMING_SNAKE_CASE__    )    )
					__a						,					__a						,					__a						:  Any									=     sentence[0], sentence[1:-1], sentence[-1]
					__a						:  List[Any]									=     sentence[: self.max_seq_length]

					__a						:  str									=     torch.zeros(self.n_classes    )
					__a						:  Optional[Any]									=     1

					__a						:  str									=     Image.open(os.path.join(self.data_dir    ,       self.data[index]['img']    )    ).convert('RGB'    )
					__a						:  int									=     self.transforms(SCREAMING_SNAKE_CASE__    )

					return {
					    "image_start_token": start_token,
					    "image_end_token": end_token,
					    "sentence": sentence,
					    "image": image,
					    "label": label,
					}



		def    __lowerCAmelCase  (					self		:    Optional[Any]    ):



					'''simple docstring'''



					__a						:  Optional[int]									=     Counter()
					for row in self.data:
								label_freqs.update(row['label']    )
					return label_freqs




def        UpperCAmelCase__		(						lowerCamelCase_       :       Any				):
			__a						:  int									=     [len(row['sentence']				) for row in batch]
			__a						,					__a						:  int									=     len(lowerCamelCase_				), max(lowerCamelCase_				)

			__a						:  Optional[int]									=     torch.zeros(lowerCamelCase_   ,  lowerCamelCase_   ,  dtype=torch.long				)
			__a						:  str									=     torch.zeros(lowerCamelCase_   ,  lowerCamelCase_   ,  dtype=torch.long				)

			for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase_   ,  lowerCamelCase_				)				):
						__a						:  Dict									=     input_row['sentence']
						__a						:  Optional[int]									=     1

			__a						:  int									=     torch.stack([row['image'] for row in batch]				)
			__a						:  Union[str, Any]									=     torch.stack([row['label'] for row in batch]				)
			__a						:  Any									=     torch.stack([row['image_start_token'] for row in batch]				)
			__a						:  Tuple									=     torch.stack([row['image_end_token'] for row in batch]				)

			return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor

def        UpperCAmelCase__		(						):
			return [
			    "Crime",
			    "Drama",
			    "Thriller",
			    "Action",
			    "Comedy",
			    "Romance",
			    "Documentary",
			    "Short",
			    "Mystery",
			    "History",
			    "Family",
			    "Adventure",
			    "Fantasy",
			    "Sci-Fi",
			    "Western",
			    "Horror",
			    "Sport",
			    "War",
			    "Music",
			    "Musical",
			    "Animation",
			    "Biography",
			    "Film-Noir",
			]

def        UpperCAmelCase__		(						):
			return transforms.Compose(
			    [
			        transforms.Resize(2_5_6				),
			        transforms.CenterCrop(2_2_4				),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.46777044, 0.44531429, 0.40661017]   ,  std=[0.12221994, 0.12145835, 0.14380469]   ,  ),
			    ]				)












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     ):
 def 	A_      ( self    : Any      )     ->      int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
 def 	A_      ( self    : Optional[Any]      )     ->      Optional[int]:
     lowerCamelCase__					,				lowerCamelCase__       : str   					=						FlaxControlNetModel.from_pretrained(
         'lllyasviel/sd-controlnet-canny'     ,	from_pt=UpperCAmelCase     ,	dtype=jnp.bfloataa      )
     lowerCamelCase__					,				lowerCamelCase__       : int   					=						FlaxStableDiffusionControlNetPipeline.from_pretrained(
         'runwayml/stable-diffusion-v1-5'     ,	controlnet=UpperCAmelCase     ,	from_pt=UpperCAmelCase     ,	dtype=jnp.bfloataa      )
     lowerCamelCase__       : int   					=						controlnet_params

     lowerCamelCase__       : Any   					=						'bird'
     lowerCamelCase__       : Dict   					=						jax.device_count()
     lowerCamelCase__       : Any   					=						pipe.prepare_text_inputs([prompts] * num_samples      )

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

     lowerCamelCase__       : List[str]   					=						jax.random.PRNGKey(0      )
     lowerCamelCase__       : Optional[int]   					=						jax.random.split(UpperCAmelCase     ,	jax.device_count()      )

     lowerCamelCase__       : List[Any]   					=						replicate(UpperCAmelCase      )
     lowerCamelCase__       : Dict   					=						shard(UpperCAmelCase      )
     lowerCamelCase__       : Any   					=						shard(UpperCAmelCase      )

     lowerCamelCase__       : Any   					=						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)

     lowerCamelCase__       : Dict   					=						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
     lowerCamelCase__       : List[str]   					=						images[0, 253:256, 253:256, -1]

     lowerCamelCase__       : Tuple   					=						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
     lowerCamelCase__       : Tuple   					=						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 	A_      ( self    : List[str]      )     ->      Union[str, Any]:
     lowerCamelCase__					,				lowerCamelCase__       : Dict   					=						FlaxControlNetModel.from_pretrained(
         'lllyasviel/sd-controlnet-openpose'     ,	from_pt=UpperCAmelCase     ,	dtype=jnp.bfloataa      )
     lowerCamelCase__					,				lowerCamelCase__       : Optional[Any]   					=						FlaxStableDiffusionControlNetPipeline.from_pretrained(
         'runwayml/stable-diffusion-v1-5'     ,	controlnet=UpperCAmelCase     ,	from_pt=UpperCAmelCase     ,	dtype=jnp.bfloataa      )
     lowerCamelCase__       : Optional[int]   					=						controlnet_params

     lowerCamelCase__       : Any   					=						'Chef in the kitchen'
     lowerCamelCase__       : str   					=						jax.device_count()
     lowerCamelCase__       : Union[str, Any]   					=						pipe.prepare_text_inputs([prompts] * num_samples      )

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

     lowerCamelCase__       : Tuple   					=						jax.random.PRNGKey(0      )
     lowerCamelCase__       : List[str]   					=						jax.random.split(UpperCAmelCase     ,	jax.device_count()      )

     lowerCamelCase__       : Dict   					=						replicate(UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						shard(UpperCAmelCase      )
     lowerCamelCase__       : Tuple   					=						shard(UpperCAmelCase      )

     lowerCamelCase__       : Union[str, Any]   					=						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)

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

     lowerCamelCase__       : Optional[int]   					=						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
     lowerCamelCase__       : int   					=						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





