import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
__lowercase							 =					logging.get_logger('''transformers.models.encodec''')

__lowercase							 =					{
    '''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''',
    '''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''',
    '''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''',
    '''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''',
}
__lowercase							 =					{
    '''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''',
    '''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''',
    '''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''',
    '''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''',
    '''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''',
    '''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''',
    '''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''',
    '''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''',
    '''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''',
    '''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''',
    '''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''',
    '''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''',
    '''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''',
    '''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''',
    '''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''',
    '''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''',
    '''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''',
    '''encoder.model.13.lstm''': '''encoder.layers.13.lstm''',
    '''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''',
}
__lowercase							 =					{
    '''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''',
    '''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''',
    '''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''',
    '''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''',
    '''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''',
    '''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''',
    '''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''',
    '''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''',
    '''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''',
    '''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''',
    '''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''',
    '''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''',
    '''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''',
    '''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''',
    '''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''',
    '''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''',
    '''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''',
    '''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''',
}
__lowercase							 =					{
    '''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''',
    '''decoder.model.1.lstm''': '''decoder.layers.1.lstm''',
    '''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''',
    '''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''',
    '''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''',
    '''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''',
    '''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''',
    '''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''',
    '''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''',
    '''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''',
    '''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''',
    '''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''',
    '''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''',
    '''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''',
    '''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''',
    '''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''',
    '''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''',
    '''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''',
    '''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''',
}
__lowercase							 =					{
    '''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''',
    '''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''',
    '''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''',
    '''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''',
    '''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''',
    '''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''',
    '''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''',
    '''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''',
    '''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''',
    '''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''',
    '''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''',
    '''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''',
    '''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''',
    '''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''',
    '''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''',
    '''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''',
    '''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''',
    '''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''',
}
__lowercase							 =					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__lowercase							 =					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__lowercase							 =					[]
__lowercase							 =					[]



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







    for attribute in key.split('''.'''	):
        __UpperCamelCase     :Optional[int]			  =		getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    if weight_type is not None:
        __UpperCamelCase     :List[Any]			  =		getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	).shape
    else:
        __UpperCamelCase     :Optional[Any]			  =		hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
            f""" {value.shape} for {full_name}"""	)

    if weight_type == "weight":
        __UpperCamelCase     :List[str]			  =		value
    elif weight_type == "weight_g":
        __UpperCamelCase     :str			  =		value
    elif weight_type == "weight_v":
        __UpperCamelCase     :Dict			  =		value
    elif weight_type == "bias":
        __UpperCamelCase     :Dict			  =		value
    elif weight_type == "running_mean":
        __UpperCamelCase     :Dict			  =		value
    elif weight_type == "running_var":
        __UpperCamelCase     :Union[str, Any]			  =		value
    elif weight_type == "num_batches_tracked":
        __UpperCamelCase     :Any			  =		value
    elif weight_type == "weight_ih_l0":
        __UpperCamelCase     :str			  =		value
    elif weight_type == "weight_hh_l0":
        __UpperCamelCase     :int			  =		value
    elif weight_type == "bias_ih_l0":
        __UpperCamelCase     :List[str]			  =		value
    elif weight_type == "bias_hh_l0":
        __UpperCamelCase     :Tuple			  =		value
    elif weight_type == "weight_ih_l1":
        __UpperCamelCase     :List[Any]			  =		value
    elif weight_type == "weight_hh_l1":
        __UpperCamelCase     :List[str]			  =		value
    elif weight_type == "bias_ih_l1":
        __UpperCamelCase     :Dict			  =		value
    elif weight_type == "bias_hh_l1":
        __UpperCamelCase     :Tuple			  =		value
    else:
        __UpperCamelCase     :int			  =		value

    logger.info(f"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."""	)



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







    for key in ignore_keys:
        if key.endswith('''.*'''	):
            if name.startswith(key[:-1]	):
                return True
        elif ".*." in key:
            __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		key.split('''.*.'''	)
            if prefix in name and suffix in name:
                return True
        elif key in name:
            return True
    return False



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







    __UpperCamelCase     :Dict			  =		[]

    if model_name == "encodec_24khz" or "encodec_32khz":
        __UpperCamelCase     :Any			  =		MAPPING_24K
    elif model_name == "encodec_48khz":
        __UpperCamelCase     :Optional[int]			  =		MAPPING_48K
    else:
        raise ValueError(f"""Unsupported model: {model_name}"""	)

    for name, value in orig_dict.items():
        if should_ignore(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
            logger.info(f"""{name} was ignored"""	)
            continue

        __UpperCamelCase     :Optional[Any]			  =		False
        for key, mapped_key in MAPPING.items():
            if "*" in key:
                __UpperCamelCase  ,       __UpperCamelCase     :Tuple			  =		key.split('''.*.'''	)
                if prefix in name and suffix in name:
                    __UpperCamelCase     :Union[str, Any]			  =		suffix

            if key in name:
                # HACK otherwise .embed gets initialized with .embed_avg too
                if key.endswith('''embed'''	) and name.endswith('''embed_avg'''	):
                    continue

                __UpperCamelCase     :Optional[Any]			  =		True
                if "*" in mapped_key:
                    __UpperCamelCase     :Tuple			  =		name.split(SCREAMING_SNAKE_CASE	)[0].split('''.'''	)[-2]
                    __UpperCamelCase     :Optional[int]			  =		mapped_key.replace('''*'''  ,			SCREAMING_SNAKE_CASE	)
                if "weight_g" in name:
                    __UpperCamelCase     :Dict			  =		'''weight_g'''
                elif "weight_v" in name:
                    __UpperCamelCase     :List[Any]			  =		'''weight_v'''
                elif "weight_ih_l0" in name:
                    __UpperCamelCase     :Optional[int]			  =		'''weight_ih_l0'''
                elif "weight_hh_l0" in name:
                    __UpperCamelCase     :Optional[Any]			  =		'''weight_hh_l0'''
                elif "bias_ih_l0" in name:
                    __UpperCamelCase     :List[Any]			  =		'''bias_ih_l0'''
                elif "bias_hh_l0" in name:
                    __UpperCamelCase     :Optional[int]			  =		'''bias_hh_l0'''
                elif "weight_ih_l1" in name:
                    __UpperCamelCase     :List[str]			  =		'''weight_ih_l1'''
                elif "weight_hh_l1" in name:
                    __UpperCamelCase     :Any			  =		'''weight_hh_l1'''
                elif "bias_ih_l1" in name:
                    __UpperCamelCase     :List[Any]			  =		'''bias_ih_l1'''
                elif "bias_hh_l1" in name:
                    __UpperCamelCase     :Optional[int]			  =		'''bias_hh_l1'''
                elif "bias" in name:
                    __UpperCamelCase     :int			  =		'''bias'''
                elif "weight" in name:
                    __UpperCamelCase     :int			  =		'''weight'''
                elif "running_mean" in name:
                    __UpperCamelCase     :List[str]			  =		'''running_mean'''
                elif "running_var" in name:
                    __UpperCamelCase     :str			  =		'''running_var'''
                elif "num_batches_tracked" in name:
                    __UpperCamelCase     :List[str]			  =		'''num_batches_tracked'''
                else:
                    __UpperCamelCase     :int			  =		None
                set_recursively(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
            continue
        if not is_used:
            unused_weights.append(SCREAMING_SNAKE_CASE	)

    logger.warning(f"""Unused weights: {unused_weights}"""	)



@torch.no_grad()
def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=None  ,			):
    '''simple docstring'''







    if config_path is not None:
        __UpperCamelCase     :Any			  =		EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
    else:
        __UpperCamelCase     :Optional[int]			  =		EncodecConfig()

    if model_name == "encodec_24khz":
        pass  # config is already correct
    elif model_name == "encodec_32khz":
        __UpperCamelCase     :List[str]			  =		[8, 5, 4, 4]
        __UpperCamelCase     :Dict			  =		[2.2]
        __UpperCamelCase     :int			  =		64
        __UpperCamelCase     :Any			  =		32_000
        __UpperCamelCase     :List[str]			  =		2_048
        __UpperCamelCase     :str			  =		False
        __UpperCamelCase     :Any			  =		False
        __UpperCamelCase     :Tuple			  =		False
    elif model_name == "encodec_48khz":
        __UpperCamelCase     :List[str]			  =		[8, 5, 4, 2]
        __UpperCamelCase     :Union[str, Any]			  =		[3.0, 6.0, 12.0, 24.0]
        __UpperCamelCase     :Tuple			  =		48_000
        __UpperCamelCase     :Dict			  =		2
        __UpperCamelCase     :Optional[Any]			  =		False
        __UpperCamelCase     :List[Any]			  =		'''time_group_norm'''
        __UpperCamelCase     :int			  =		True
        __UpperCamelCase     :int			  =		1.0
        __UpperCamelCase     :str			  =		0.01
    else:
        raise ValueError(f"""Unknown model name: {model_name}"""	)

    __UpperCamelCase     :str			  =		EncodecModel(SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :Union[str, Any]			  =		EncodecFeatureExtractor(
        feature_size=config.audio_channels  ,			sampling_rate=config.sampling_rate  ,			chunk_length_s=config.chunk_length_s  ,			overlap=config.overlap  ,			)
    feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :Union[str, Any]			  =		torch.load(SCREAMING_SNAKE_CASE	)
    if "best_state" in original_checkpoint:
        # we might have a training state saved, in which case discard the yaml results and just retain the weights
        __UpperCamelCase     :List[str]			  =		original_checkpoint['''best_state''']
    recursively_load_weights(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    model.save_pretrained(SCREAMING_SNAKE_CASE	)

    if repo_id:
        print('''Pushing to the hub...'''	)
        feature_extractor.push_to_hub(SCREAMING_SNAKE_CASE	)
        model.push_to_hub(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    __lowercase							 =					argparse.ArgumentParser()
    parser.add_argument(
        '''--model''',
        default='''encodec_24khz''',
        type=str,
        help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''',
    )
    parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
    parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
    parser.add_argument(
        '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
    )
    parser.add_argument(
        '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
    )

    __lowercase							 =					parser.parse_args()
    convert_checkpoint(
        args.model,
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )






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







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

        # image encoding components

        __UpperCamelCase     :Union[str, Any]			  =		CLIPImageProcessor(crop_size=32 , size=32)

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

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

        torch.manual_seed(0)
        __UpperCamelCase     :Dict			  =		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     :List[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     :Tuple			  =		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     :List[str]			  =		AutoencoderKL()

        __UpperCamelCase     :Tuple			  =		{
            # 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)       ->	str:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase     :Union[str, Any]			  =		torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase     :int			  =		torch.Generator(device=__lowercase).manual_seed(__lowercase)

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

        if pil_image:
            __UpperCamelCase     :List[Any]			  =		input_image * 0.5 + 0.5
            __UpperCamelCase     :Optional[Any]			  =		input_image.clamp(0 , 1)
            __UpperCamelCase     :int			  =		input_image.cpu().permute(0 , 2 , 3 , 1).float().numpy()
            __UpperCamelCase     :Optional[Any]			  =		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)       ->	Union[str, Any]:
        __UpperCamelCase     :Dict			  =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase     :Tuple			  =		self.get_dummy_components()
        __UpperCamelCase     :Any			  =		StableUnCLIPImgaImgPipeline(**__lowercase)
        __UpperCamelCase     :Optional[Any]			  =		sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

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

        assert image.shape == (1, 32, 32, 3)
        __UpperCamelCase     :List[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)       ->	str:
        __UpperCamelCase     :Optional[Any]			  =		torch_device in ['''cpu''', '''mps''']

        self._test_attention_slicing_forward_pass(test_max_difference=__lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Optional[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)       ->	Union[str, Any]:
        self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__lowercase)


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






    '''simple docstring'''






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







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

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

        __UpperCamelCase     :Dict			  =		output.images[0]

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

        assert_mean_pixel_difference(__lowercase , __lowercase)







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

        __UpperCamelCase     :Any			  =		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     :Any			  =		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     :Optional[int]			  =		pipe(__lowercase , '''anime turle''' , generator=__lowercase , output_type='''np''')

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

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

        assert_mean_pixel_difference(__lowercase , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :Dict			  =		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     :List[Any]			  =		StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa)
        __UpperCamelCase     :Union[str, Any]			  =		pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase     :Optional[Any]			  =		pipe(
            __lowercase , '''anime turtle''' , num_inference_steps=2 , output_type='''np''' , )

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





