





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     lowercase__       (	__lowerCamelCase			,				__lowerCamelCase			,				__lowerCamelCase			,				unittest.TestCase				):

   '''simple docstring'''


   a						:							Union[str, Any]   		=		StableDiffusionControlNetImgaImgPipeline
   a						:							Tuple   		=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
   a						:							Dict   		=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   a						:							Dict   		=		IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}				)
   a						:							Optional[Any]   		=		IMAGE_TO_IMAGE_IMAGE_PARAMS




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       torch.manual_seed(0							)
       UpperCamelCase__							:				Tuple          =      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							)
       UpperCamelCase__							:				int          =      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							)
       UpperCamelCase__							:				List[Any]          =      DDIMScheduler(
           beta_start=0.0_0085,		beta_end=0.012,		beta_schedule='''scaled_linear''',		clip_sample=__magic_name__,		set_alpha_to_one=__magic_name__,		)
       torch.manual_seed(0							)
       UpperCamelCase__							:				List[Any]          =      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							)
       UpperCamelCase__							:				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=1000,		)
       UpperCamelCase__							:				Any          =      CLIPTextModel(__magic_name__							)
       UpperCamelCase__							:				str          =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

       UpperCamelCase__							:				str          =      {
           '''unet''': unet,
           '''controlnet''': controlnet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=0							) ->      Tuple:






       """simple docstring"""
       if str(__magic_name__							).startswith('''mps'''							):
           UpperCamelCase__							:				int          =      torch.manual_seed(__magic_name__							)
       else:
           UpperCamelCase__							:				Union[str, Any]          =      torch.Generator(device=__magic_name__							).manual_seed(__magic_name__							)

       UpperCamelCase__							:				Any          =      2
       UpperCamelCase__							:				Optional[int]          =      randn_tensor(
           (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),		generator=__magic_name__,		device=torch.device(__magic_name__							),		)
       UpperCamelCase__							:				Tuple          =      floats_tensor(control_image.shape,		rng=random.Random(__magic_name__							)							).to(__magic_name__							)
       UpperCamelCase__							:				List[str]          =      image.cpu().permute(0,		2,		3,		1							)[0]
       UpperCamelCase__							:				List[str]          =      Image.fromarray(np.uinta(__magic_name__							)							).convert('''RGB'''							).resize((64, 64)							)
       UpperCamelCase__							:				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       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       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       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3							)




   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       self._test_inference_batch_single_identical(expected_max_diff=2E-3							)






class     lowercase__       (	__lowerCamelCase			,				__lowerCamelCase			,				unittest.TestCase				):

   '''simple docstring'''


   a						:							Tuple   		=		StableDiffusionControlNetImgaImgPipeline
   a						:							Dict   		=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
   a						:							str   		=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   a						:							Dict   		=		frozenset([]				)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       torch.manual_seed(0							)
       UpperCamelCase__							:				Union[str, 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							)

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

       UpperCamelCase__							:				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),		)
       controlneta.controlnet_down_blocks.apply(__magic_name__							)

       torch.manual_seed(0							)
       UpperCamelCase__							:				List[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(__magic_name__							)

       torch.manual_seed(0							)
       UpperCamelCase__							:				Optional[Any]          =      DDIMScheduler(
           beta_start=0.0_0085,		beta_end=0.012,		beta_schedule='''scaled_linear''',		clip_sample=__magic_name__,		set_alpha_to_one=__magic_name__,		)
       torch.manual_seed(0							)
       UpperCamelCase__							:				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							)
       UpperCamelCase__							:				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=1000,		)
       UpperCamelCase__							:				Dict          =      CLIPTextModel(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

       UpperCamelCase__							:				Tuple          =      MultiControlNetModel([controlneta, controlneta]							)

       UpperCamelCase__							:				List[str]          =      {
           '''unet''': unet,
           '''controlnet''': controlnet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=0							) ->      str:






       """simple docstring"""
       if str(__magic_name__							).startswith('''mps'''							):
           UpperCamelCase__							:				str          =      torch.manual_seed(__magic_name__							)
       else:
           UpperCamelCase__							:				str          =      torch.Generator(device=__magic_name__							).manual_seed(__magic_name__							)

       UpperCamelCase__							:				Optional[int]          =      2

       UpperCamelCase__							:				List[str]          =      [
           randn_tensor(
               (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),		generator=__magic_name__,		device=torch.device(__magic_name__							),		),
           randn_tensor(
               (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),		generator=__magic_name__,		device=torch.device(__magic_name__							),		),
       ]

       UpperCamelCase__							:				Dict          =      floats_tensor(control_image[0].shape,		rng=random.Random(__magic_name__							)							).to(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      image.cpu().permute(0,		2,		3,		1							)[0]
       UpperCamelCase__							:				List[str]          =      Image.fromarray(np.uinta(__magic_name__							)							).convert('''RGB'''							).resize((64, 64)							)
       UpperCamelCase__							:				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       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      self.get_dummy_components()
       UpperCamelCase__							:				Union[str, Any]          =      self.pipeline_class(**__magic_name__							)
       pipe.to(__magic_name__							)

       UpperCamelCase__							:				Tuple          =      10.0
       UpperCamelCase__							:				Optional[Any]          =      4

       UpperCamelCase__							:				Union[str, Any]          =      self.get_dummy_inputs(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      steps
       UpperCamelCase__							:				Dict          =      scale
       UpperCamelCase__							:				str          =      pipe(**__magic_name__							)[0]

       UpperCamelCase__							:				Tuple          =      self.get_dummy_inputs(__magic_name__							)
       UpperCamelCase__							:				Any          =      steps
       UpperCamelCase__							:				Optional[Any]          =      scale
       UpperCamelCase__							:				List[str]          =      pipe(**__magic_name__,		control_guidance_start=0.1,		control_guidance_end=0.2							)[0]

       UpperCamelCase__							:				List[str]          =      self.get_dummy_inputs(__magic_name__							)
       UpperCamelCase__							:				int          =      steps
       UpperCamelCase__							:				Tuple          =      scale
       UpperCamelCase__							:				str          =      pipe(**__magic_name__,		control_guidance_start=[0.1, 0.3],		control_guidance_end=[0.2, 0.7]							)[0]

       UpperCamelCase__							:				Dict          =      self.get_dummy_inputs(__magic_name__							)
       UpperCamelCase__							:				Optional[int]          =      steps
       UpperCamelCase__							:				Tuple          =      scale
       UpperCamelCase__							:				List[str]          =      pipe(**__magic_name__,		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       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       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       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3							)




   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       self._test_inference_batch_single_identical(expected_max_diff=2E-3							)




   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      self.get_dummy_components()
       UpperCamelCase__							:				str          =      self.pipeline_class(**__magic_name__							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)
       with tempfile.TemporaryDirectory() as tmpdir:
           try:
               # save_pretrained is not implemented for Multi-ControlNet
               pipe.save_pretrained(__magic_name__							)
           except NotImplementedError:
               pass






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

   '''simple docstring'''


   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''							)

       UpperCamelCase__							:				Any          =      StableDiffusionControlNetImgaImgPipeline.from_pretrained(
           '''runwayml/stable-diffusion-v1-5''',		safety_checker=__magic_name__,		controlnet=__magic_name__							)
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:				List[Any]          =      torch.Generator(device='''cpu'''							).manual_seed(0							)
       UpperCamelCase__							:				str          =      '''evil space-punk bird'''
       UpperCamelCase__							:				List[str]          =      load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''							).resize((512, 512)							)
       UpperCamelCase__							:				int          =      load_image(
           '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''							).resize((512, 512)							)

       UpperCamelCase__							:				Optional[int]          =      pipe(
           __magic_name__,		__magic_name__,		control_image=__magic_name__,		generator=__magic_name__,		output_type='''np''',		num_inference_steps=50,		strength=0.6,		)

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

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

       UpperCamelCase__							:				Tuple          =      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 absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


a_  :				str						= """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

a_  :				int						= """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

a_  :				Tuple						= """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,						_KWARGS_DESCRIPTION			)
class      snake_case    (    datasets.Metric			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   return datasets.MetricInfo(
       description=_DESCRIPTION			,  citation=_CITATION			,  inputs_description=_KWARGS_DESCRIPTION			,  features=datasets.Features(
           {
               "predictions": datasets.Value("string"			,  id="sequence"     ),
               "references": datasets.Value("string"			,  id="sequence"     ),
           }     )			,  codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"]			,  reference_urls=[
           "https://en.wikipedia.org/wiki/ROUGE_(metric)",
           "https://github.com/google-research/google-research/tree/master/rouge",
       ]			,  )







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  UpperCamelCase=True			,  UpperCamelCase=False     ):







   """simple docstring"""
   if rouge_types is None:
    lowerCamelCase_      =  ["rouge1", "rouge2", "rougeL", "rougeLsum"]

   lowerCamelCase_      =  rouge_scorer.RougeScorer(rouge_types=UpperCamelCase			,  use_stemmer=UpperCamelCase     )
   if use_aggregator:
    lowerCamelCase_      =  scoring.BootstrapAggregator()
   else:
    lowerCamelCase_      =  []

   for ref, pred in zip(UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  scorer.score(UpperCamelCase			,  UpperCamelCase     )
    if use_aggregator:
     aggregator.add_scores(UpperCamelCase     )
    else:
     scores.append(UpperCamelCase     )

   if use_aggregator:
    lowerCamelCase_      =  aggregator.aggregate()
   else:
    lowerCamelCase_      =  {}
    for key in scores[0]:
     lowerCamelCase_      =  [score[key] for score in scores]

   return result



