


"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


snake_case    :   str	    =					logging.get_logger(__name__)
def     __lowerCamelCase						(			UpperCAmelCase_						:		int						):




      """simple docstring"""





      if isinstance(__lowerCAmelCase			,  np.ndarray						):
            return list(tensor.shape						)

      a						:int										=	tf.shape(__lowerCAmelCase						)

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

      a						:Optional[Any]										=	tensor.shape.as_list()

      return [dynamic[i] if s is None else s for i, s in enumerate(__lowerCAmelCase						)]
def     __lowerCamelCase						(			UpperCAmelCase_						:		Union[str, Any]			,  UpperCAmelCase_						:		List[str] = None			,  UpperCAmelCase_						:		int = None						):




      """simple docstring"""





      return tf.nn.softmax(logits=logits + 1E-9			,  axis=__lowerCAmelCase			,  name=__lowerCAmelCase						)
def     __lowerCamelCase						(			UpperCAmelCase_						:		Dict			,  UpperCAmelCase_						:		str			,  UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		Dict=1E-5			,  UpperCAmelCase_						:		str=-1						):




      """simple docstring"""





      if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCAmelCase			,  __lowerCAmelCase						):
            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
      a						:Optional[Any]										=	tf.nn.moments(__lowerCAmelCase			,  axes=[axis]			,  keepdims=__lowerCAmelCase						)

      if axis != -1:
            # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
            # on every dimension except axis
            a						:Dict										=	[1] * inputs.shape.rank
            a						:Optional[int]										=	shape_list(__lowerCAmelCase						)[axis]
            a						:List[str]										=	tf.reshape(__lowerCAmelCase			,  __lowerCAmelCase						)
            a						:Tuple										=	tf.reshape(__lowerCAmelCase			,  __lowerCAmelCase						)

      # Compute layer normalization using the batch_normalization
      # function.
      a						:Dict										=	tf.nn.batch_normalization(
          __lowerCAmelCase			,  __lowerCAmelCase			,  __lowerCAmelCase			,  offset=__lowerCAmelCase			,  scale=__lowerCAmelCase			,  variance_epsilon=__lowerCAmelCase			,  )
      return outputs
def     __lowerCamelCase						(			UpperCAmelCase_						:		str			,  UpperCAmelCase_						:		Optional[int]=0			,  UpperCAmelCase_						:		Optional[int]=-1						):




      """simple docstring"""





      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

      a						:Optional[Any]										=	tf.shape(__lowerCAmelCase						)
      a						:Union[str, Any]										=	tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]						)
      a						:List[Any]										=	tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]			,  axis=0						)
      return tf.reshape(__lowerCAmelCase			,  __lowerCAmelCase						)
def     __lowerCamelCase						(			UpperCAmelCase_						:		List[Any]						):




      """simple docstring"""





      if not isinstance(__lowerCAmelCase			,  tf.Tensor						):
            a						:List[Any]										=	tf.convert_to_tensor(__lowerCAmelCase						)  # Catches stray NumPy inputs
      if encoder_attention_mask.shape.rank == 3:
            a						:List[Any]										=	encoder_attention_mask[:, None, :, :]
      if encoder_attention_mask.shape.rank == 2:
            a						:Dict										=	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))
      a						:List[str]										=	(
          tf.cast(1			,  encoder_attention_mask.dtype						) - encoder_extended_attention_mask
      ) * encoder_extended_attention_mask.dtype.min

      return encoder_extended_attention_mask
def     __lowerCamelCase						(			UpperCAmelCase_						:		Union[str, Any]			,  UpperCAmelCase_						:		Optional[Any]			,  UpperCAmelCase_						:		str = "input_ids"						):




      """simple docstring"""





      tf.debugging.assert_less(
          __lowerCAmelCase			,  tf.cast(__lowerCAmelCase			,  dtype=tensor.dtype						)			,  message=(
              F'''The maximum value of {tensor_name} ({tf.math.reduce_max(__lowerCAmelCase						)}) must be smaller than the embedding '''
              F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
          )			,  )
def     __lowerCamelCase						(			UpperCAmelCase_						:		int			,  UpperCAmelCase_						:		List[str]			,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      a						:str										=	6_4512
      # 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.
      a						:List[str]										=	[x for x in data if len(__lowerCAmelCase						) > 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}'''						)

      a						:Optional[int]										=	np.asarray(__lowerCAmelCase						)

      a						:List[str]										=	1
      a						:List[Any]										=	np.array_split(__lowerCAmelCase			,  __lowerCAmelCase						)

      # 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
            a						:Any										=	np.array_split(__lowerCAmelCase			,  __lowerCAmelCase						)

      if num_chunks > 1:
            for chunk_id, chunk_data in enumerate(__lowerCAmelCase						):
                  a						:int										=	chunk_data
      else:
            a						:Any										=	data
def     __lowerCamelCase						(			UpperCAmelCase_						:		Union[str, Any]			,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      if name in group.attrs:
            a						:str										=	[n.decode('''utf8'''						) if hasattr(__lowerCAmelCase			,  '''decode'''						) else n for n in group.attrs[name]]
      else:
            a						:Union[str, Any]										=	[]
            a						:str										=	0
            while "%s%d" % (name, chunk_id) in group.attrs:
                  data.extend(
                      [n.decode('''utf8'''						) if hasattr(__lowerCAmelCase			,  '''decode'''						) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]						)
                  chunk_id += 1
      return data






def     __lowerCamelCase						(			UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      def _expand_single_ad_tensor(UpperCAmelCase_						:		Optional[int]						):
            if isinstance(__lowerCAmelCase			,  tf.Tensor						) and t.shape.rank == 1:
                  return tf.expand_dims(__lowerCAmelCase			,  axis=-1						)
            return t

      return tf.nest.map_structure(_expand_single_ad_tensor			,  __lowerCAmelCase						)









import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 		_snake_case  (				_snake_case						,   _snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	CycleDiffusionPipeline
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     'negative_prompt',
     'height',
     'width',
     'negative_prompt_embeds',
 }
 SCREAMING_SNAKE_CASE__				=	PipelineTesterMixin.required_optional_params - {'latents'}
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}							)
 SCREAMING_SNAKE_CASE__				=	IMAGE_TO_IMAGE_IMAGE_PARAMS
 SCREAMING_SNAKE_CASE__				=	IMAGE_TO_IMAGE_IMAGE_PARAMS




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.manual_seed(0   )
       a						:Optional[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 ,  )
       a						:List[str]										=	DDIMScheduler(
           beta_start=0.0_0085 ,  beta_end=0.012 ,  beta_schedule='''scaled_linear''' ,  num_train_timesteps=1000 ,  clip_sample=_lowerCamelCase ,  set_alpha_to_one=_lowerCamelCase ,  )
       torch.manual_seed(0   )
       a						: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   )
       a						:Dict										=	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 ,  )
       a						:str										=	CLIPTextModel(_lowerCamelCase   )
       a						:List[str]										=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       a						:Union[str, Any]										=	{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=0   ):
       a						:Tuple										=	floats_tensor((1, 3, 32, 32) ,  rng=random.Random(_lowerCamelCase   )   ).to(_lowerCamelCase   )
       a						:Tuple										=	image / 2 + 0.5
       if str(_lowerCamelCase   ).startswith('''mps'''   ):
             a						:List[str]										=	torch.manual_seed(_lowerCamelCase   )
       else:
             a						:Any										=	torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )
       a						:int										=	{
           '''prompt''': '''An astronaut riding an elephant''',
           '''source_prompt''': '''An astronaut riding a horse''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''eta''': 0.1,
           '''strength''': 0.8,
           '''guidance_scale''': 3,
           '''source_guidance_scale''': 1,
           '''output_type''': '''numpy''',
       }
       return inputs




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Union[str, Any]										=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       a						:Optional[Any]										=	self.get_dummy_components()
       a						:Dict										=	CycleDiffusionPipeline(**_lowerCamelCase   )
       a						:Optional[Any]										=	pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:List[str]										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:Any										=	pipe(**_lowerCamelCase   )
       a						:List[Any]										=	output.images

       a						:str										=	images[0, -3:, -3:, -1]

       assert images.shape == (1, 32, 32, 3)
       a						:List[Any]										=	np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2




 @unittest.skipIf(torch_device != '''cuda''' ,  '''This test requires a GPU'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[int]										=	self.get_dummy_components()
       for name, module in components.items():
             if hasattr(_lowerCamelCase ,  '''half'''   ):
                   a						:Union[str, Any]										=	module.half()
       a						:List[Any]										=	CycleDiffusionPipeline(**_lowerCamelCase   )
       a						:Dict										=	pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:Tuple										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:Optional[int]										=	pipe(**_lowerCamelCase   )
       a						:Optional[Any]										=	output.images

       a						:List[Any]										=	images[0, -3:, -3:, -1]

       assert images.shape == (1, 32, 32, 3)
       a						:str										=	np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_save_load_local()




 @unittest.skip('''non-deterministic pipeline'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_inference_batch_single_identical()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_dict_tuple_outputs_equivalent()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_save_load_optional_components()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_attention_slicing_forward_pass()







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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/cycle-diffusion/black_colored_car.png'''   )
       a						:Optional[int]										=	load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''   )
       a						:Optional[Any]										=	init_image.resize((512, 512)   )

       a						:List[str]										=	'''CompVis/stable-diffusion-v1-4'''
       a						:List[str]										=	DDIMScheduler.from_pretrained(_lowerCamelCase ,  subfolder='''scheduler'''   )
       a						:Tuple										=	CycleDiffusionPipeline.from_pretrained(
           _lowerCamelCase ,  scheduler=_lowerCamelCase ,  safety_checker=_lowerCamelCase ,  torch_dtype=torch.floataa ,  revision='''fp16'''   )

       pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )
       pipe.enable_attention_slicing()

       a						:Optional[Any]										=	'''A black colored car'''
       a						:Any										=	'''A blue colored car'''

       a						:str										=	torch.manual_seed(0   )
       a						:List[Any]										=	pipe(
           prompt=_lowerCamelCase ,  source_prompt=_lowerCamelCase ,  image=_lowerCamelCase ,  num_inference_steps=100 ,  eta=0.1 ,  strength=0.85 ,  guidance_scale=3 ,  source_guidance_scale=1 ,  generator=_lowerCamelCase ,  output_type='''np''' ,  )
       a						:int										=	output.images

       # the values aren't exactly equal, but the images look the same visually
       assert np.abs(image - expected_image   ).max() < 5e-1




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/cycle-diffusion/black_colored_car.png'''   )
       a						:Optional[int]										=	load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''   )
       a						:List[str]										=	init_image.resize((512, 512)   )

       a						:List[str]										=	'''CompVis/stable-diffusion-v1-4'''
       a						:Any										=	DDIMScheduler.from_pretrained(_lowerCamelCase ,  subfolder='''scheduler'''   )
       a						:int										=	CycleDiffusionPipeline.from_pretrained(_lowerCamelCase ,  scheduler=_lowerCamelCase ,  safety_checker=_lowerCamelCase   )

       pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )
       pipe.enable_attention_slicing()

       a						:Optional[int]										=	'''A black colored car'''
       a						:Any										=	'''A blue colored car'''

       a						:Optional[int]										=	torch.manual_seed(0   )
       a						:Union[str, Any]										=	pipe(
           prompt=_lowerCamelCase ,  source_prompt=_lowerCamelCase ,  image=_lowerCamelCase ,  num_inference_steps=100 ,  eta=0.1 ,  strength=0.85 ,  guidance_scale=3 ,  source_guidance_scale=1 ,  generator=_lowerCamelCase ,  output_type='''np''' ,  )
       a						:Optional[int]										=	output.images

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





