

"""simple docstring"""




from scipy.stats import pearsonr

import datasets


__UpperCAmelCase =				'\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'


__UpperCAmelCase =				'\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


__UpperCAmelCase =				'\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'







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

		def    __lowercase       (  self		:			str    ):






			'''simple docstring'''
			return datasets.MetricInfo(
			    description=_DESCRIPTION					,citation=_CITATION					,inputs_description=_KWARGS_DESCRIPTION					,features=datasets.Features(
			        {
			            """predictions""": datasets.Value("""float"""    ),
			            """references""": datasets.Value("""float"""    ),
			        }    )					,reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"""]					,)




		def    __lowercase       (  self		:			Union[str, Any]					,A		:			str					,A		:			List[Any]					,A		:			Any=False    ):






			'''simple docstring'''
			if return_pvalue:
				UpperCAmelCase__					:  Optional[Any]					   =					pearsonr(A					,A    )
				return {"pearsonr": results[0], "p-value": results[1]}
			else:
				return {"pearsonr": float(pearsonr(A					,A    )[0]    )}








"""simple docstring"""




from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


__UpperCAmelCase =				logging.get_logger(__name__)  # pylint: disable=invalid-name

__UpperCAmelCase =				'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase=8      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Tuple					   =					h // scale_factor**2
 if h % scale_factor**2 != 0:
  new_h += 1
 UpperCAmelCase__					:  Any					   =					w // scale_factor**2
 if w % scale_factor**2 != 0:
  new_w += 1
 return new_h * scale_factor, new_w * scale_factor







class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			str					,A		:			MultilingualCLIP					,A		:			XLMRobertaTokenizer					,A		:			UNetaDConditionModel					,A		:			Union[DDIMScheduler, DDPMScheduler]					,A		:			VQModel					,):






   '''simple docstring'''
   super().__init__()

   self.register_modules(
       text_encoder=A					,tokenizer=A					,unet=A					,scheduler=A					,movq=A					,)
   UpperCAmelCase__					:  Optional[Any]					   =					2 ** (len(self.movq.config.block_out_channels    ) - 1)

  def    __lowercase       (  self		:			Dict					,A		:			Any					,A		:			Tuple					,A		:			Dict					,A		:			int					,A		:			str					,A		:			List[str]    ):






   '''simple docstring'''
   if latents is None:
    UpperCAmelCase__					:  Any					   =					randn_tensor(A					,generator=A					,device=A					,dtype=A    )
   else:
    if latents.shape != shape:
     raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"    )
    UpperCAmelCase__					:  int					   =					latents.to(A    )

   UpperCAmelCase__					:  Any					   =					latents * scheduler.init_noise_sigma
   return latents

  def    __lowercase       (  self		:			Optional[int]					,A		:			List[Any]					,A		:			Optional[Any]					,A		:			str					,A		:			Optional[Any]					,A		:			str=None					,):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					len(A    ) if isinstance(A					,A    ) else 1
   # get prompt text embeddings
   UpperCAmelCase__					:  List[Any]					   =					self.tokenizer(
       A					,padding="""max_length"""					,truncation=A					,max_length=77					,return_attention_mask=A					,add_special_tokens=A					,return_tensors="""pt"""					,)

   UpperCAmelCase__					:  List[str]					   =					text_inputs.input_ids
   UpperCAmelCase__					:  Any					   =					self.tokenizer(A					,padding="""longest"""					,return_tensors="""pt"""    ).input_ids

   if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A					,A    ):
    UpperCAmelCase__					:  List[str]					   =					self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]    )
    logger.warning(
        """The following part of your input was truncated because CLIP can only handle sequences up to"""
        f" {self.tokenizer.model_max_length} tokens: {removed_text}"    )

   UpperCAmelCase__					:  str					   =					text_input_ids.to(A    )
   UpperCAmelCase__					:  Optional[Any]					   =					text_inputs.attention_mask.to(A    )

   UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					self.text_encoder(
       input_ids=A					,attention_mask=A    )

   UpperCAmelCase__					:  Optional[int]					   =					prompt_embeds.repeat_interleave(A					,dim=0    )
   UpperCAmelCase__					:  Optional[int]					   =					text_encoder_hidden_states.repeat_interleave(A					,dim=0    )
   UpperCAmelCase__					:  List[str]					   =					text_mask.repeat_interleave(A					,dim=0    )

   if do_classifier_free_guidance:
    UpperCAmelCase__					:  List[str]
    if negative_prompt is None:
     UpperCAmelCase__					:  List[Any]					   =					[""""""] * batch_size
    elif type(A    ) is not type(A    ):
     raise TypeError(
         f"`negative_prompt` should be the same type to `prompt`, but got {type(A    )} !="
         f" {type(A    )}."    )
    elif isinstance(A					,A    ):
     UpperCAmelCase__					:  Any					   =					[negative_prompt]
    elif batch_size != len(A    ):
     raise ValueError(
         f"`negative_prompt`: {negative_prompt} has batch size {len(A    )}, but `prompt`:"
         f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
         """ the batch size of `prompt`."""    )
    else:
     UpperCAmelCase__					:  List[Any]					   =					negative_prompt

    UpperCAmelCase__					:  Any					   =					self.tokenizer(
        A					,padding="""max_length"""					,max_length=77					,truncation=A					,return_attention_mask=A					,add_special_tokens=A					,return_tensors="""pt"""					,)
    UpperCAmelCase__					:  Optional[int]					   =					uncond_input.input_ids.to(A    )
    UpperCAmelCase__					:  str					   =					uncond_input.attention_mask.to(A    )

    UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					self.text_encoder(
        input_ids=A					,attention_mask=A    )

    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

    UpperCAmelCase__					:  Any					   =					negative_prompt_embeds.shape[1]
    UpperCAmelCase__					:  Any					   =					negative_prompt_embeds.repeat(1					,A    )
    UpperCAmelCase__					:  str					   =					negative_prompt_embeds.view(batch_size * num_images_per_prompt					,A    )

    UpperCAmelCase__					:  Dict					   =					uncond_text_encoder_hidden_states.shape[1]
    UpperCAmelCase__					:  Any					   =					uncond_text_encoder_hidden_states.repeat(1					,A					,1    )
    UpperCAmelCase__					:  Any					   =					uncond_text_encoder_hidden_states.view(
        batch_size * num_images_per_prompt					,A					,-1    )
    UpperCAmelCase__					:  List[Any]					   =					uncond_text_mask.repeat_interleave(A					,dim=0    )

    # done duplicates

    # For classifier free guidance, we need to do two forward passes.
    # Here we concatenate the unconditional and text embeddings into a single batch
    # to avoid doing two forward passes
    UpperCAmelCase__					:  Any					   =					torch.cat([negative_prompt_embeds, prompt_embeds]    )
    UpperCAmelCase__					:  int					   =					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]    )

    UpperCAmelCase__					:  str					   =					torch.cat([uncond_text_mask, text_mask]    )

   return prompt_embeds, text_encoder_hidden_states, text_mask

  def    __lowercase       (  self		:			Tuple					,A		:			Dict=0    ):






   '''simple docstring'''
   if is_accelerate_available():
    from accelerate import cpu_offload
   else:
    raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

   UpperCAmelCase__					:  str					   =					torch.device(f"cuda:{gpu_id}"    )

   UpperCAmelCase__					:  Tuple					   =					[
       self.unet,
       self.text_encoder,
       self.movq,
   ]
   for cpu_offloaded_model in models:
    if cpu_offloaded_model is not None:
     cpu_offload(A					,A    )

  def    __lowercase       (  self		:			int					,A		:			Optional[Any]=0    ):






   '''simple docstring'''
   if is_accelerate_available() and is_accelerate_version(""">="""					,"""0.17.0.dev0"""    ):
    from accelerate import cpu_offload_with_hook
   else:
    raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""    )

   UpperCAmelCase__					:  List[str]					   =					torch.device(f"cuda:{gpu_id}"    )

   if self.device.type != "cpu":
    self.to("""cpu"""					,silence_dtype_warnings=A    )
    torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

   UpperCAmelCase__					:  List[str]					   =					None
   for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Union[str, Any]					   =					cpu_offload_with_hook(A					,A					,prev_module_hook=A    )

   if self.safety_checker is not None:
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					cpu_offload_with_hook(self.safety_checker					,A					,prev_module_hook=A    )

   # We'll offload the last model manually.
   UpperCAmelCase__					:  Any					   =					hook

  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   if not hasattr(self.unet					,"""_hf_hook"""    ):
    return self.device
   for module in self.unet.modules():
    if (
        hasattr(A					,"""_hf_hook"""    )
        and hasattr(module._hf_hook					,"""execution_device"""    )
        and module._hf_hook.execution_device is not None
    ):
     return torch.device(module._hf_hook.execution_device    )
   return self.device




  @torch.no_grad()
  @replace_example_docstring(A    )
  def __call__(  self		:			Union[str, Any]					,A		:			Union[str, List[str]]					,A		:			Union[torch.FloatTensor, List[torch.FloatTensor]]					,A		:			Union[torch.FloatTensor, List[torch.FloatTensor]]					,A		:			Optional[Union[str, List[str]]] = None					,A		:			int = 512					,A		:			int = 512					,A		:			int = 100					,A		:			float = 4.0					,A		:			int = 1					,A		:			Optional[Union[torch.Generator, List[torch.Generator]]] = None					,A		:			Optional[torch.FloatTensor] = None					,A		:			Optional[str] = "pil"					,A		:			bool = True					,):






   '''simple docstring'''

   if isinstance(A					,A    ):
    UpperCAmelCase__					:  str					   =					1
   elif isinstance(A					,A    ):
    UpperCAmelCase__					:  Tuple					   =					len(A    )
   else:
    raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(A    )}"    )

   UpperCAmelCase__					:  Optional[int]					   =					self._execution_device

   UpperCAmelCase__					:  Dict					   =					batch_size * num_images_per_prompt
   UpperCAmelCase__					:  Union[str, Any]					   =					guidance_scale > 1.0

   UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					self._encode_prompt(
       A					,A					,A					,A					,A    )

   if isinstance(A					,A    ):
    UpperCAmelCase__					:  Dict					   =					torch.cat(A					,dim=0    )
   if isinstance(A					,A    ):
    UpperCAmelCase__					:  Dict					   =					torch.cat(A					,dim=0    )

   if do_classifier_free_guidance:
    UpperCAmelCase__					:  Optional[int]					   =					image_embeds.repeat_interleave(A					,dim=0    )
    UpperCAmelCase__					:  Tuple					   =					negative_image_embeds.repeat_interleave(A					,dim=0    )

   UpperCAmelCase__					:  List[str]					   =					torch.cat([negative_image_embeds, image_embeds]					,dim=0    ).to(
       dtype=prompt_embeds.dtype					,device=A    )

   self.scheduler.set_timesteps(A					,device=A    )
   UpperCAmelCase__					:  int					   =					self.scheduler.timesteps

   UpperCAmelCase__					:  Union[str, Any]					   =					self.unet.config.in_channels

   UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					get_new_h_w(A					,A					,self.movq_scale_factor    )

   # create initial latent
   UpperCAmelCase__					:  Dict					   =					self.prepare_latents(
       (batch_size, num_channels_latents, height, width)					,text_encoder_hidden_states.dtype					,A					,A					,A					,self.scheduler					,)

   for i, t in enumerate(self.progress_bar(A    )    ):
    # expand the latents if we are doing classifier free guidance
    UpperCAmelCase__					:  Optional[Any]					   =					torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

    UpperCAmelCase__					:  Optional[Any]					   =					{"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
    UpperCAmelCase__					:  Any					   =					self.unet(
        sample=A					,timestep=A					,encoder_hidden_states=A					,added_cond_kwargs=A					,return_dict=A					,)[0]

    if do_classifier_free_guidance:
     UpperCAmelCase__   ,      UpperCAmelCase__					:  List[str]					   =					noise_pred.split(latents.shape[1]					,dim=1    )
     UpperCAmelCase__   ,      UpperCAmelCase__					:  Any					   =					noise_pred.chunk(2    )
     UpperCAmelCase__   ,      UpperCAmelCase__					:  str					   =					variance_pred.chunk(2    )
     UpperCAmelCase__					:  int					   =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
     UpperCAmelCase__					:  List[Any]					   =					torch.cat([noise_pred, variance_pred_text]					,dim=1    )

    if not (
        hasattr(self.scheduler.config					,"""variance_type"""    )
        and self.scheduler.config.variance_type in ["learned", "learned_range"]
    ):
     UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					noise_pred.split(latents.shape[1]					,dim=1    )

    # compute the previous noisy sample x_t -> x_t-1
    UpperCAmelCase__					:  Optional[Any]					   =					self.scheduler.step(
        A					,A					,A					,generator=A					,).prev_sample
   # post-processing
   UpperCAmelCase__					:  List[Any]					   =					self.movq.decode(A					,force_not_quantize=A    )["""sample"""]

   if output_type not in ["pt", "np", "pil"]:
    raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"    )

   if output_type in ["np", "pil"]:
    UpperCAmelCase__					:  Union[str, Any]					   =					image * 0.5 + 0.5
    UpperCAmelCase__					:  Optional[Any]					   =					image.clamp(0					,1    )
    UpperCAmelCase__					:  Dict					   =					image.cpu().permute(0					,2					,3					,1    ).float().numpy()

   if output_type == "pil":
    UpperCAmelCase__					:  List[str]					   =					self.numpy_to_pil(A    )

   if not return_dict:
    return (image,)

   return ImagePipelineOutput(images=A    )





