
'''simple docstring'''
import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						StableDiffusionControlNetImgaImgPipeline
 UpperCAmelCase_		    =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
 UpperCAmelCase_		    =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 UpperCAmelCase_		    =						IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}					)
 UpperCAmelCase_		    =						IMAGE_TO_IMAGE_IMAGE_PARAMS





 def        snake_case__		(    self):
     torch.manual_seed(0)
     snake_case_ :    List[Any]     			=						UNetaDConditionModel(
         block_out_channels=(32, 64) ,       layers_per_block=2 ,       sample_size=32 ,       in_channels=4 ,       out_channels=4 ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,       cross_attention_dim=32 ,       )
     torch.manual_seed(0)
     snake_case_ :    Dict     			=						ControlNetModel(
         block_out_channels=(32, 64) ,       layers_per_block=2 ,       in_channels=4 ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,       cross_attention_dim=32 ,       conditioning_embedding_out_channels=(16, 32) ,       )
     torch.manual_seed(0)
     snake_case_ :    Optional[Any]     			=						DDIMScheduler(
         beta_start=0.00_085 ,       beta_end=0.012 ,       beta_schedule="scaled_linear" ,       clip_sample=lowercase_ ,       set_alpha_to_one=lowercase_ ,       )
     torch.manual_seed(0)
     snake_case_ :    List[str]     			=						AutoencoderKL(
         block_out_channels=[32, 64] ,       in_channels=3 ,       out_channels=3 ,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,       latent_channels=4 ,       )
     torch.manual_seed(0)
     snake_case_ :    str     			=						CLIPTextConfig(
         bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=32 ,       intermediate_size=37 ,       layer_norm_eps=1E-05 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=10_00 ,       )
     snake_case_ :    Optional[Any]     			=						CLIPTextModel(lowercase_)
     snake_case_ :    str     			=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

     snake_case_ :    Optional[int]     			=						{
         "unet": unet,
         "controlnet": controlnet,
         "scheduler": scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }
     return components





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=0):
     if str(lowercase_).startswith("mps"):
         snake_case_ :    Dict     			=						torch.manual_seed(lowercase_)
     else:
         snake_case_ :    List[str]     			=						torch.Generator(device=lowercase_).manual_seed(lowercase_)

     snake_case_ :    Dict     			=						2
     snake_case_ :    str     			=						randn_tensor(
         (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,       generator=lowercase_ ,       device=torch.device(lowercase_) ,       )
     snake_case_ :    int     			=						floats_tensor(control_image.shape ,       rng=random.Random(lowercase_)).to(lowercase_)
     snake_case_ :    int     			=						image.cpu().permute(0 ,       2 ,       3 ,       1)[0]
     snake_case_ :    Dict     			=						Image.fromarray(np.uinta(lowercase_)).convert("RGB").resize((64, 64))
     snake_case_ :    Optional[Any]     			=						{
         "prompt": "A painting of a squirrel eating a burger",
         "generator": generator,
         "num_inference_steps": 2,
         "guidance_scale": 6.0,
         "output_type": "numpy",
         "image": image,
         "control_image": control_image,
     }

     return inputs





 def        snake_case__		(    self):
     return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3)





 @unittest.skipIf(
     torch_device != "cuda" or not is_xformers_available() ,       reason="XFormers attention is only available with CUDA and `xformers` installed" ,       )
 def        snake_case__		(    self):
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3)





 def        snake_case__		(    self):
     self._test_inference_batch_single_identical(expected_max_diff=2E-3)






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						StableDiffusionControlNetImgaImgPipeline
 UpperCAmelCase_		    =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
 UpperCAmelCase_		    =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 UpperCAmelCase_		    =						frozenset([]					)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess





 def        snake_case__		(    self):
     torch.manual_seed(0)
     snake_case_ :    Optional[int]     			=						UNetaDConditionModel(
         block_out_channels=(32, 64) ,       layers_per_block=2 ,       sample_size=32 ,       in_channels=4 ,       out_channels=4 ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,       cross_attention_dim=32 ,       )
     torch.manual_seed(0)

     def init_weights(lowercase_):
         if isinstance(lowercase_ ,       torch.nn.Convad):
             torch.nn.init.normal(m.weight)
             m.bias.data.fill_(1.0)

     snake_case_ :    Optional[Any]     			=						ControlNetModel(
         block_out_channels=(32, 64) ,       layers_per_block=2 ,       in_channels=4 ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,       cross_attention_dim=32 ,       conditioning_embedding_out_channels=(16, 32) ,       )
     controlneta.controlnet_down_blocks.apply(lowercase_)

     torch.manual_seed(0)
     snake_case_ :    Union[str, Any]     			=						ControlNetModel(
         block_out_channels=(32, 64) ,       layers_per_block=2 ,       in_channels=4 ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,       cross_attention_dim=32 ,       conditioning_embedding_out_channels=(16, 32) ,       )
     controlneta.controlnet_down_blocks.apply(lowercase_)

     torch.manual_seed(0)
     snake_case_ :    Optional[Any]     			=						DDIMScheduler(
         beta_start=0.00_085 ,       beta_end=0.012 ,       beta_schedule="scaled_linear" ,       clip_sample=lowercase_ ,       set_alpha_to_one=lowercase_ ,       )
     torch.manual_seed(0)
     snake_case_ :    int     			=						AutoencoderKL(
         block_out_channels=[32, 64] ,       in_channels=3 ,       out_channels=3 ,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,       latent_channels=4 ,       )
     torch.manual_seed(0)
     snake_case_ :    Any     			=						CLIPTextConfig(
         bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=32 ,       intermediate_size=37 ,       layer_norm_eps=1E-05 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=10_00 ,       )
     snake_case_ :    Any     			=						CLIPTextModel(lowercase_)
     snake_case_ :    Tuple     			=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

     snake_case_ :    Union[str, Any]     			=						MultiControlNetModel([controlneta, controlneta])

     snake_case_ :    int     			=						{
         "unet": unet,
         "controlnet": controlnet,
         "scheduler": scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }
     return components





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=0):
     if str(lowercase_).startswith("mps"):
         snake_case_ :    Optional[int]     			=						torch.manual_seed(lowercase_)
     else:
         snake_case_ :    Tuple     			=						torch.Generator(device=lowercase_).manual_seed(lowercase_)

     snake_case_ :    List[Any]     			=						2

     snake_case_ :    Union[str, Any]     			=						[
         randn_tensor(
             (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,       generator=lowercase_ ,       device=torch.device(lowercase_) ,       ),
         randn_tensor(
             (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,       generator=lowercase_ ,       device=torch.device(lowercase_) ,       ),
     ]

     snake_case_ :    Tuple     			=						floats_tensor(control_image[0].shape ,       rng=random.Random(lowercase_)).to(lowercase_)
     snake_case_ :    Optional[Any]     			=						image.cpu().permute(0 ,       2 ,       3 ,       1)[0]
     snake_case_ :    List[Any]     			=						Image.fromarray(np.uinta(lowercase_)).convert("RGB").resize((64, 64))
     snake_case_ :    List[Any]     			=						{
         "prompt": "A painting of a squirrel eating a burger",
         "generator": generator,
         "num_inference_steps": 2,
         "guidance_scale": 6.0,
         "output_type": "numpy",
         "image": image,
         "control_image": control_image,
     }

     return inputs





 def        snake_case__		(    self):
     snake_case_ :    str     			=						self.get_dummy_components()
     snake_case_ :    Optional[int]     			=						self.pipeline_class(**lowercase_)
     pipe.to(lowercase_)

     snake_case_ :    Tuple     			=						10.0
     snake_case_ :    List[Any]     			=						4

     snake_case_ :    str     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    Any     			=						steps
     snake_case_ :    Tuple     			=						scale
     snake_case_ :    Dict     			=						pipe(**lowercase_)[0]

     snake_case_ :    int     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    Optional[Any]     			=						steps
     snake_case_ :    Tuple     			=						scale
     snake_case_ :    Optional[int]     			=						pipe(**lowercase_ ,       control_guidance_start=0.1 ,       control_guidance_end=0.2)[0]

     snake_case_ :    int     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    str     			=						steps
     snake_case_ :    Dict     			=						scale
     snake_case_ :    int     			=						pipe(**lowercase_ ,       control_guidance_start=[0.1, 0.3] ,       control_guidance_end=[0.2, 0.7])[0]

     snake_case_ :    List[str]     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    Tuple     			=						steps
     snake_case_ :    int     			=						scale
     snake_case_ :    Union[str, Any]     			=						pipe(**lowercase_ ,       control_guidance_start=0.4 ,       control_guidance_end=[0.5, 0.8])[0]

     # make sure that all outputs are different
     assert np.sum(np.abs(output_a - output_a)) > 1E-3
     assert np.sum(np.abs(output_a - output_a)) > 1E-3
     assert np.sum(np.abs(output_a - output_a)) > 1E-3





 def        snake_case__		(    self):
     return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3)





 @unittest.skipIf(
     torch_device != "cuda" or not is_xformers_available() ,       reason="XFormers attention is only available with CUDA and `xformers` installed" ,       )
 def        snake_case__		(    self):
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3)





 def        snake_case__		(    self):
     self._test_inference_batch_single_identical(expected_max_diff=2E-3)





 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self.get_dummy_components()
     snake_case_ :    Optional[Any]     			=						self.pipeline_class(**lowercase_)
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     with tempfile.TemporaryDirectory() as tmpdir:
         try:
             # save_pretrained is not implemented for Multi-ControlNet
             pipe.save_pretrained(lowercase_)
         except NotImplementedError:
             pass






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





 def        snake_case__		(    self):
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()





 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")

     snake_case_ :    Optional[Any]     			=						StableDiffusionControlNetImgaImgPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5" ,       safety_checker=lowercase_ ,       controlnet=lowercase_)
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    str     			=						torch.Generator(device="cpu").manual_seed(0)
     snake_case_ :    List[Any]     			=						"evil space-punk bird"
     snake_case_ :    Dict     			=						load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png").resize((5_12, 5_12))
     snake_case_ :    Optional[int]     			=						load_image(
         "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png").resize((5_12, 5_12))

     snake_case_ :    List[str]     			=						pipe(
         lowercase_ ,       lowercase_ ,       control_image=lowercase_ ,       generator=lowercase_ ,       output_type="np" ,       num_inference_steps=50 ,       strength=0.6 ,       )

     snake_case_ :    str     			=						output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     snake_case_ :    List[str]     			=						load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy")

     assert np.abs(expected_image - image).max() < 9E-2


'''simple docstring'''
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


a_											=						logging.get_logger(__name__)

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						r"\w+[.]\d+"
    snake_case_ :    Tuple     			=						re.findall(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    for pat in pats:
        snake_case_ :    Union[str, Any]     			=						key.replace(__SCREAMING_SNAKE_CASE,		"_".join(pat.split("."       )       )       )
    return key

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Tuple     			=						pt_tuple_key[:-1] + ("scale",)
    if (
        any("norm" in str_ for str_ in pt_tuple_key       )
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        snake_case_ :    int     			=						pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        snake_case_ :    Dict     			=						pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        snake_case_ :    List[Any]     			=						pt_tuple_key[:-1] + ("embedding",)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    snake_case_ :    List[str]     			=						pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        snake_case_ :    str     			=						pt_tensor.transpose(2,		3,		1,		0       )
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    snake_case_ :    str     			=						pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight":
        snake_case_ :    Any     			=						pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    snake_case_ :    Union[str, Any]     			=						pt_tuple_key[:-1] + ("weight",)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    snake_case_ :    Union[str, Any]     			=						pt_tuple_key[:-1] + ("bias",)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor


def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=4_2       ):
    """simple docstring"""






    snake_case_ :    int     			=						{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    snake_case_ :    Optional[int]     			=						flax_model.init_weights(PRNGKey(__SCREAMING_SNAKE_CASE       )       )

    snake_case_ :    Union[str, Any]     			=						flatten_dict(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        snake_case_ :    List[str]     			=						rename_key(__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Tuple     			=						tuple(renamed_pt_key.split("."       )       )

        # Correctly rename weight parameters
        snake_case_			, snake_case_ :    Dict     			=						rename_key_and_reshape_tensor(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                    f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'       )

        # also add unexpected weight so that warning is thrown
        snake_case_ :    Any     			=						jnp.asarray(__SCREAMING_SNAKE_CASE       )

    return unflatten_dict(__SCREAMING_SNAKE_CASE       )




