


'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_a   :   List[Any]												=   logging.get_logger(__name__)





def   _a		(lowercase__			:   Union[tf.Tensor, np.ndarray]						)			->			List[int]:






     """simple docstring"""
     if isinstance(lowercase__							,   np.ndarray						):
          return list(tensor.shape						)

     __snake_case           =						tf.shape(lowercase__						)

     if tensor.shape == tf.TensorShape(lowercase__						):
          return dynamic

     __snake_case           =						tensor.shape.as_list()

     return [dynamic[i] if s is None else s for i, s in enumerate(lowercase__						)]





def   _a		(lowercase__			:   tf.Tensor							,   lowercase__			:   Optional[int] = None							,   lowercase__			:   Optional[str] = None						)			->			tf.Tensor:






     """simple docstring"""
     return tf.nn.softmax(logits=logits + 1e-9							,   axis=lowercase__							,   name=lowercase__						)





def   _a		(lowercase__			:   Optional[Any]							,   lowercase__			:   Tuple							,   lowercase__			:   int							,   lowercase__			:   int=1e-5							,   lowercase__			:   int=-1						)			->			int:






     """simple docstring"""
     # This is a very simplified functional layernorm, designed to duplicate
     # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
     # models in Transformers.

     if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowercase__							,   lowercase__						):
          raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.'						)

     # Get mean and variance on the axis to be normalized
     __snake_case      ,       __snake_case           =						tf.nn.moments(lowercase__							,   axes=[axis]							,   keepdims=lowercase__						)

     if axis != -1:
          # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
          # on every dimension except axis
          __snake_case           =						[1] * inputs.shape.rank
          __snake_case           =						shape_list(lowercase__						)[axis]
          __snake_case           =						tf.reshape(lowercase__							,   lowercase__						)
          __snake_case           =						tf.reshape(lowercase__							,   lowercase__						)

     # Compute layer normalization using the batch_normalization
     # function.
     __snake_case           =						tf.nn.batch_normalization(
         lowercase__							,   lowercase__							,   lowercase__							,   offset=lowercase__							,   scale=lowercase__							,   variance_epsilon=lowercase__							,   )
     return outputs





def   _a		(lowercase__			:   str							,   lowercase__			:   Optional[int]=0							,   lowercase__			:   List[str]=-1						)			->			int:






     """simple docstring"""
     # Replicates the behavior of torch.flatten in TF

     # If end_dim or start_dim is negative, count them from the end
     if end_dim < 0:
          end_dim += input.shape.rank
     if start_dim < 0:
          start_dim += input.shape.rank

     if start_dim == end_dim:
          return input

     __snake_case           =						tf.shape(lowercase__						)
     __snake_case           =						tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]						)
     __snake_case           =						tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]							,   axis=0						)
     return tf.reshape(lowercase__							,   lowercase__						)





def   _a		(lowercase__			:   tf.Tensor						)			->			tf.Tensor:






     """simple docstring"""
     if not isinstance(lowercase__							,   tf.Tensor						):
          __snake_case           =						tf.convert_to_tensor(lowercase__						)  # Catches stray NumPy inputs
     if encoder_attention_mask.shape.rank == 3:
          __snake_case           =						encoder_attention_mask[:, None, :, :]
     if encoder_attention_mask.shape.rank == 2:
          __snake_case           =						encoder_attention_mask[:, None, None, :]
     # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
     # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
     # /transformer/transformer_layers.py#L270
     # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
     # encoder_extended_attention_mask.transpose(-1, -2))
     __snake_case           =						(
         tf.cast(1							,   encoder_attention_mask.dtype						) - encoder_extended_attention_mask
     ) * encoder_extended_attention_mask.dtype.min

     return encoder_extended_attention_mask





def   _a		(lowercase__			:   tf.Tensor							,   lowercase__			:   int							,   lowercase__			:   str = "input_ids"						)			->			None:






     """simple docstring"""
     tf.debugging.assert_less(
         lowercase__							,   tf.cast(lowercase__							,   dtype=tensor.dtype						)							,   message=(
             f'The maximum value of {tensor_name} ({tf.math.reduce_max(lowercase__						)}) must be smaller than the embedding '
             f'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
         )							,   )





def   _a		(lowercase__			:   int							,   lowercase__			:   Union[str, Any]							,   lowercase__			:   Union[str, Any]						)			->			Optional[int]:






     """simple docstring"""
     __snake_case           =						6_4_5_1_2
     # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
     # because in that case even chunking the array would not make the saving
     # possible.
     __snake_case           =						[x for x in data if len(lowercase__						) > HDF5_OBJECT_HEADER_LIMIT]

     # Expecting this to never be true.
     if bad_attributes:
          raise RuntimeError(
              'The following attributes cannot be saved to HDF5 file because '
              f'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
              f'bytes: {bad_attributes}'						)

     __snake_case           =						np.asarray(lowercase__						)

     __snake_case           =						1
     __snake_case           =						np.array_split(lowercase__							,   lowercase__						)

     # This will never loop forever thanks to the test above.
     while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data						):
          num_chunks += 1
          __snake_case           =						np.array_split(lowercase__							,   lowercase__						)

     if num_chunks > 1:
          for chunk_id, chunk_data in enumerate(lowercase__						):
               __snake_case           =						chunk_data
     else:
          __snake_case           =						data





def   _a		(lowercase__			:   Dict							,   lowercase__			:   Optional[int]						)			->			Optional[int]:






     """simple docstring"""
     if name in group.attrs:
          __snake_case           =						[n.decode('utf8'						) if hasattr(lowercase__							,   'decode'						) else n for n in group.attrs[name]]
     else:
          __snake_case           =						[]
          __snake_case           =						0
          while "%s%d" % (name, chunk_id) in group.attrs:
               data.extend(
                   [n.decode('utf8'						) if hasattr(lowercase__							,   'decode'						) else n for n in group.attrs['%s%d' % (name, chunk_id)]]						)
               chunk_id += 1
     return data





def   _a		(lowercase__			:   str						)			->			List[str]:






     """simple docstring"""

     def _expand_single_ad_tensor(lowercase__			:   str						):
          if isinstance(lowercase__							,   tf.Tensor						) and t.shape.rank == 1:
               return tf.expand_dims(lowercase__							,   axis=-1						)
          return t

     return tf.nest.map_structure(_expand_single_ad_tensor							,   lowercase__						)






# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

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        _SCREAMING_SNAKE_CASE	( snake_case_     ,			snake_case_     ,			snake_case_     ,			unittest.TestCase		):
	lowerCAmelCase__   =					StableDiffusionControlNetImgaImgPipeline
	lowerCAmelCase__   =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
	lowerCAmelCase__   =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	lowerCAmelCase__   =					IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}		)
	lowerCAmelCase__   =					IMAGE_TO_IMAGE_IMAGE_PARAMS



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					torch.manual_seed(0					)
					lowerCamelCase_         =     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					)
					lowerCamelCase_         =     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					)
					lowerCamelCase_         =     DDIMScheduler(
					    beta_start=0.0_0_0_8_5		,    beta_end=0.0_1_2		,    beta_schedule="scaled_linear"		,    clip_sample=lowercase		,    set_alpha_to_one=lowercase		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     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					)
					lowerCamelCase_         =     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=1000		,    )
					lowerCamelCase_         =     CLIPTextModel(lowercase					)
					lowerCamelCase_         =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

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



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase=0					)							->			int:
					if str(lowercase					).startswith("mps"					):
									lowerCamelCase_         =     torch.manual_seed(lowercase					)
					else:
									lowerCamelCase_         =     torch.Generator(device=lowercase					).manual_seed(lowercase					)

					lowerCamelCase_         =     2
					lowerCamelCase_         =     randn_tensor(
					    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,    generator=lowercase		,    device=torch.device(lowercase					)		,    )
					lowerCamelCase_         =     floats_tensor(control_image.shape		,    rng=random.Random(lowercase					)					).to(lowercase					)
					lowerCamelCase_         =     image.cpu().permute(0		,    2		,    3		,    1					)[0]
					lowerCamelCase_         =     Image.fromarray(np.uinta(lowercase					)					).convert("RGB"					).resize((64, 64)					)
					lowerCamelCase_         =     {
					    "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        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					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        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					self._test_inference_batch_single_identical(expected_max_diff=2e-3					)




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



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					torch.manual_seed(0					)
					lowerCamelCase_         =     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					)

					lowerCamelCase_         =     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					)
					lowerCamelCase_         =     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					)
					lowerCamelCase_         =     DDIMScheduler(
					    beta_start=0.0_0_0_8_5		,    beta_end=0.0_1_2		,    beta_schedule="scaled_linear"		,    clip_sample=lowercase		,    set_alpha_to_one=lowercase		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     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					)
					lowerCamelCase_         =     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=1000		,    )
					lowerCamelCase_         =     CLIPTextModel(lowercase					)
					lowerCamelCase_         =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

					lowerCamelCase_         =     MultiControlNetModel([controlneta, controlneta]					)

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



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase=0					)							->			List[Any]:
					if str(lowercase					).startswith("mps"					):
									lowerCamelCase_         =     torch.manual_seed(lowercase					)
					else:
									lowerCamelCase_         =     torch.Generator(device=lowercase					).manual_seed(lowercase					)

					lowerCamelCase_         =     2

					lowerCamelCase_         =     [
					    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					)		,    ),
					]

					lowerCamelCase_         =     floats_tensor(control_image[0].shape		,    rng=random.Random(lowercase					)					).to(lowercase					)
					lowerCamelCase_         =     image.cpu().permute(0		,    2		,    3		,    1					)[0]
					lowerCamelCase_         =     Image.fromarray(np.uinta(lowercase					)					).convert("RGB"					).resize((64, 64)					)
					lowerCamelCase_         =     {
					    "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        SCREAMING_SNAKE_CASE_(       self					)							->			List[Any]:
					lowerCamelCase_         =     self.get_dummy_components()
					lowerCamelCase_         =     self.pipeline_class(**lowercase					)
					pipe.to(lowercase					)

					lowerCamelCase_         =     1_0.0
					lowerCamelCase_         =     4

					lowerCamelCase_         =     self.get_dummy_inputs(lowercase					)
					lowerCamelCase_         =     steps
					lowerCamelCase_         =     scale
					lowerCamelCase_         =     pipe(**lowercase					)[0]

					lowerCamelCase_         =     self.get_dummy_inputs(lowercase					)
					lowerCamelCase_         =     steps
					lowerCamelCase_         =     scale
					lowerCamelCase_         =     pipe(**lowercase		,    control_guidance_start=0.1		,    control_guidance_end=0.2					)[0]

					lowerCamelCase_         =     self.get_dummy_inputs(lowercase					)
					lowerCamelCase_         =     steps
					lowerCamelCase_         =     scale
					lowerCamelCase_         =     pipe(**lowercase		,    control_guidance_start=[0.1, 0.3]		,    control_guidance_end=[0.2, 0.7]					)[0]

					lowerCamelCase_         =     self.get_dummy_inputs(lowercase					)
					lowerCamelCase_         =     steps
					lowerCamelCase_         =     scale
					lowerCamelCase_         =     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        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					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        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					self._test_inference_batch_single_identical(expected_max_diff=2e-3					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					lowerCamelCase_         =     self.get_dummy_components()
					lowerCamelCase_         =     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        _SCREAMING_SNAKE_CASE	( unittest.TestCase		):



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[Any]:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					lowerCamelCase_         =     ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"					)

					lowerCamelCase_         =     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					)

					lowerCamelCase_         =     torch.Generator(device="cpu"					).manual_seed(0					)
					lowerCamelCase_         =     "evil space-punk bird"
					lowerCamelCase_         =     load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"					).resize((512, 512)					)
					lowerCamelCase_         =     load_image(
					    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"					).resize((512, 512)					)

					lowerCamelCase_         =     pipe(
					    lowercase		,    lowercase		,    control_image=lowercase		,    generator=lowercase		,    output_type="np"		,    num_inference_steps=50		,    strength=0.6		,    )

					lowerCamelCase_         =     output.images[0]

					assert image.shape == (512, 512, 3)

					lowerCamelCase_         =     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


