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


__lowercase											=      {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 			lowerCamelCase_  (     nn.Module				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase) ->			str:
        super().__init__()
        __UpperCamelCase  :Dict    						=	torchvision.models.resnetaaa(pretrained=__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(model.children())[:-2]
        __UpperCamelCase  :Dict    						=	nn.Sequential(*__lowercase)
        __UpperCamelCase  :List[str]    						=	nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Tuple:
        # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
        __UpperCamelCase  :List[Any]    						=	self.pool(self.model(__lowercase))
        __UpperCamelCase  :List[str]    						=	torch.flatten(__lowercase				,				start_dim=2)
        __UpperCamelCase  :List[str]    						=	out.transpose(1				,				2).contiguous()
        return out  # BxNx2048


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase) ->			int:
        __UpperCamelCase  :List[Any]    						=	[json.loads(__lowercase) for l in open(__lowercase)]
        __UpperCamelCase  :List[str]    						=	os.path.dirname(__lowercase)
        __UpperCamelCase  :List[str]    						=	tokenizer
        __UpperCamelCase  :Optional[Any]    						=	labels
        __UpperCamelCase  :str    						=	len(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	max_seq_length

        __UpperCamelCase  :Optional[int]    						=	transforms







    def __len__(			self) ->			str:
        return len(self.data)







    def __getitem__(			self				,				__lowercase) ->			Optional[Any]:
        __UpperCamelCase  :Dict    						=	torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				,				add_special_tokens=__lowercase))
        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	sentence[0], sentence[1:-1], sentence[-1]
        __UpperCamelCase  :Tuple    						=	sentence[: self.max_seq_length]

        __UpperCamelCase  :Optional[int]    						=	torch.zeros(self.n_classes)
        __UpperCamelCase  :int    						=	1

        __UpperCamelCase  :Dict    						=	Image.open(os.path.join(self.data_dir				,				self.data[index]['''img'''])).convert('''RGB''')
        __UpperCamelCase  :int    						=	self.transforms(__lowercase)

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







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :str    						=	Counter()
        for row in self.data:
            label_freqs.update(row['''label'''])
        return label_freqs



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	[len(row['''sentence''']			) for row in batch]
    __UpperCamelCase						, __UpperCamelCase  :Any    						=	len(SCREAMING_SNAKE_CASE			), max(SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Tuple    						=	torch.zeros(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    dtype=torch.long			)
    __UpperCamelCase  :Optional[Any]    						=	torch.zeros(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    dtype=torch.long			)

    for i_batch, (input_row, length) in enumerate(zip(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)			):
        __UpperCamelCase  :Dict    						=	input_row['''sentence''']
        __UpperCamelCase  :Dict    						=	1

    __UpperCamelCase  :List[Any]    						=	torch.stack([row['''image'''] for row in batch]			)
    __UpperCamelCase  :Dict    						=	torch.stack([row['''label'''] for row in batch]			)
    __UpperCamelCase  :Optional[Any]    						=	torch.stack([row['''image_start_token'''] for row in batch]			)
    __UpperCamelCase  :Union[str, Any]    						=	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 lowerCamelCase						(     ):
    '''simple docstring'''







    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 lowerCamelCase						(     ):
    '''simple docstring'''







    return transforms.Compose(
        [
            transforms.Resize(256			),
            transforms.CenterCrop(224			),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]						,    std=[0.12_221_994, 0.12_145_835, 0.14_380_469]						,    ),
        ]			)






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class 			lowerCamelCase_  (     UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		List[str]      			=   StableUnCLIPImgaImgPipeline
    a__			:		Optional[Any]      			=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    a__			:		Optional[int]      			=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a__			:		str      			=   frozenset(
        []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    a__			:		List[Any]      			=   frozenset([]				)







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Optional[Any]    						=	32
        __UpperCamelCase  :int    						=	embedder_hidden_size

        # image encoding components

        __UpperCamelCase  :Dict    						=	CLIPImageProcessor(crop_size=32				,				size=32)

        torch.manual_seed(0)
        __UpperCamelCase  :Tuple    						=	CLIPVisionModelWithProjection(
            CLIPVisionConfig(
                hidden_size=__lowercase				,				projection_dim=__lowercase				,				num_hidden_layers=5				,				num_attention_heads=4				,				image_size=32				,				intermediate_size=37				,				patch_size=1				,				))

        # regular denoising components

        torch.manual_seed(0)
        __UpperCamelCase  :Union[str, Any]    						=	StableUnCLIPImageNormalizer(embedding_dim=__lowercase)
        __UpperCamelCase  :Any    						=	DDPMScheduler(beta_schedule='''squaredcos_cap_v2''')

        torch.manual_seed(0)
        __UpperCamelCase  :List[Any]    						=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        torch.manual_seed(0)
        __UpperCamelCase  :str    						=	CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0				,				eos_token_id=2				,				hidden_size=__lowercase				,				projection_dim=32				,				intermediate_size=37				,				layer_norm_eps=1E-0_5				,				num_attention_heads=4				,				num_hidden_layers=5				,				pad_token_id=1				,				vocab_size=1_000				,				))

        torch.manual_seed(0)
        __UpperCamelCase  :Any    						=	UNetaDConditionModel(
            sample_size=32				,				in_channels=4				,				out_channels=4				,				down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')				,				up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')				,				block_out_channels=(32, 64)				,				attention_head_dim=(2, 4)				,				class_embed_type='''projection'''				,				projection_class_embeddings_input_dim=embedder_projection_dim * 2				,				cross_attention_dim=__lowercase				,				layers_per_block=1				,				upcast_attention=__lowercase				,				use_linear_projection=__lowercase				,				)

        torch.manual_seed(0)
        __UpperCamelCase  :Dict    						=	DDIMScheduler(
            beta_schedule='''scaled_linear'''				,				beta_start=0.0_00_85				,				beta_end=0.0_12				,				prediction_type='''v_prediction'''				,				set_alpha_to_one=__lowercase				,				steps_offset=1				,				)

        torch.manual_seed(0)
        __UpperCamelCase  :Any    						=	AutoencoderKL()

        __UpperCamelCase  :Dict    						=	{
            # image encoding components
            '''feature_extractor''': feature_extractor,
            '''image_encoder''': image_encoder.eval(),
            # image noising components
            '''image_normalizer''': image_normalizer.eval(),
            '''image_noising_scheduler''': image_noising_scheduler,
            # regular denoising components
            '''tokenizer''': tokenizer,
            '''text_encoder''': text_encoder.eval(),
            '''unet''': unet.eval(),
            '''scheduler''': scheduler,
            '''vae''': vae.eval(),
        }

        return components







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=0				,				__lowercase=True) ->			Tuple:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase  :int    						=	torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase  :int    						=	torch.Generator(device=__lowercase).manual_seed(__lowercase)

        __UpperCamelCase  :Tuple    						=	floats_tensor((1, 3, 32, 32)				,				rng=random.Random(__lowercase)).to(__lowercase)

        if pil_image:
            __UpperCamelCase  :Tuple    						=	input_image * 0.5 + 0.5
            __UpperCamelCase  :Any    						=	input_image.clamp(0				,				1)
            __UpperCamelCase  :Any    						=	input_image.cpu().permute(0				,				2				,				3				,				1).float().numpy()
            __UpperCamelCase  :int    						=	DiffusionPipeline.numpy_to_pil(__lowercase)[0]

        return {
            "prompt": "An anime racoon running a marathon",
            "image": input_image,
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "np",
        }







    @skip_mps
    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :int    						=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase  :Dict    						=	self.get_dummy_components()
        __UpperCamelCase  :Dict    						=	StableUnCLIPImgaImgPipeline(**__lowercase)
        __UpperCamelCase  :int    						=	sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :Optional[int]    						=	self.get_dummy_inputs(__lowercase)
        inputs.update({'''image_embeds''': None})
        __UpperCamelCase  :Any    						=	sd_pipe(**__lowercase).images
        __UpperCamelCase  :int    						=	image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __UpperCamelCase  :Union[str, Any]    						=	np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3







    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Union[str, Any]    						=	torch_device in ['''cpu''', '''mps''']

        self._test_attention_slicing_forward_pass(test_max_difference=__lowercase)







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :List[Any]    						=	torch_device in ['''cpu''', '''mps''']

        self._test_inference_batch_single_identical(test_max_difference=__lowercase)







    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available()				,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,				)
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__lowercase)


@slow
@require_torch_gpu
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :Union[str, Any]    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase  :str    						=	load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy''')

        __UpperCamelCase  :Optional[int]    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-l-img2img'''				,				torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :Union[str, Any]    						=	torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase  :str    						=	pipe(__lowercase				,				'''anime turle'''				,				generator=__lowercase				,				output_type='''np''')

        __UpperCamelCase  :List[str]    						=	output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :Dict    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase  :str    						=	load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy''')

        __UpperCamelCase  :Tuple    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img'''				,				torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :int    						=	torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase  :int    						=	pipe(__lowercase				,				'''anime turle'''				,				generator=__lowercase				,				output_type='''np''')

        __UpperCamelCase  :List[str]    						=	output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :List[Any]    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __UpperCamelCase  :Any    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img'''				,				torch_dtype=torch.floataa)
        __UpperCamelCase  :Any    						=	pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :Tuple    						=	pipe(
            __lowercase				,				'''anime turtle'''				,				num_inference_steps=2				,				output_type='''np'''				,				)

        __UpperCamelCase  :str    						=	torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9





