import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


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





def 		__lowerCamelCase							(self   :							Optional[Any]					,       UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							Tuple					,       UpperCAmelCase__   :							Dict					,       UpperCAmelCase__   :							List[str]     ):
					SCREAMING_SNAKE_CASE				=				image.to(device=UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				image  # take image as image_embeddings
					SCREAMING_SNAKE_CASE				=				image_embeddings.unsqueeze(1     )

					# duplicate image embeddings for each generation per prompt, using mps friendly method
					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				image_embeddings.shape
					SCREAMING_SNAKE_CASE				=				image_embeddings.repeat(1					,       UpperCAmelCase__					,       1     )
					SCREAMING_SNAKE_CASE				=				image_embeddings.view(bs_embed * num_images_per_prompt					,       UpperCAmelCase__					,       -1     )

					if do_classifier_free_guidance:
										SCREAMING_SNAKE_CASE				=				torch.zeros_like(UpperCAmelCase__     )

										# 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
										SCREAMING_SNAKE_CASE				=				torch.cat([uncond_embeddings, image_embeddings]     )

					return image_embeddings


class   lowercase			(   a   ):




							def __init__(  self      :    Any		,							_UpperCamelCase      :    PriorTransformer		,							_UpperCamelCase      :    CLIPTokenizer		,							_UpperCamelCase      :    CLIPTextModelWithProjection		,							_UpperCamelCase      :    UnCLIPScheduler		,							_UpperCamelCase      :    Optional[dict] = None		,							)    -> str:



												'''simple docstring'''

												super().__init__()

												SCREAMING_SNAKE_CASE				=				{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

												SCREAMING_SNAKE_CASE				=				decoder_pipe_kwargs.get("torch_dtype"		,							_UpperCamelCase					) or prior.dtype

												SCREAMING_SNAKE_CASE				=				StableDiffusionImageVariationPipeline.from_pretrained(
												    "lambdalabs/sd-image-variations-diffusers"		,							**_UpperCamelCase					)

												# replace `_encode_image` method
												SCREAMING_SNAKE_CASE				=				types.MethodType(_encode_image		,							self.decoder_pipe					)

												self.register_modules(
												    prior=_UpperCamelCase		,							tokenizer=_UpperCamelCase		,							text_encoder=_UpperCamelCase		,							prior_scheduler=_UpperCamelCase		,							)




							def 		__snake_case(  self      :    Dict		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    Optional[Union[CLIPTextModelOutput, Tuple]] = None		,							_UpperCamelCase      :    Optional[torch.Tensor] = None		,							)    -> Union[str, Any]:



												'''simple docstring'''

												if text_model_output is None:
																	SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					) if isinstance(_UpperCamelCase		,							_UpperCamelCase					) else 1
																	# get prompt text embeddings
																	SCREAMING_SNAKE_CASE				=				self.tokenizer(
																	    _UpperCamelCase		,							padding="max_length"		,							max_length=self.tokenizer.model_max_length		,							return_tensors="pt"		,							)
																	SCREAMING_SNAKE_CASE				=				text_inputs.input_ids
																	SCREAMING_SNAKE_CASE				=				text_inputs.attention_mask.bool().to(_UpperCamelCase					)

																	if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																						SCREAMING_SNAKE_CASE				=				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
																						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}"					)
																						SCREAMING_SNAKE_CASE				=				text_input_ids[:, : self.tokenizer.model_max_length]

																	SCREAMING_SNAKE_CASE				=				self.text_encoder(text_input_ids.to(_UpperCamelCase					)					)

																	SCREAMING_SNAKE_CASE				=				text_encoder_output.text_embeds
																	SCREAMING_SNAKE_CASE				=				text_encoder_output.last_hidden_state

												else:
																	SCREAMING_SNAKE_CASE				=				text_model_output[0].shape[0]
																	SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				text_model_output[0], text_model_output[1]
																	SCREAMING_SNAKE_CASE				=				text_attention_mask

												SCREAMING_SNAKE_CASE				=				text_embeddings.repeat_interleave(_UpperCamelCase		,							dim=0					)
												SCREAMING_SNAKE_CASE				=				text_encoder_hidden_states.repeat_interleave(_UpperCamelCase		,							dim=0					)
												SCREAMING_SNAKE_CASE				=				text_mask.repeat_interleave(_UpperCamelCase		,							dim=0					)

												if do_classifier_free_guidance:
																	SCREAMING_SNAKE_CASE				=				[""] * batch_size

																	SCREAMING_SNAKE_CASE				=				self.tokenizer(
																	    _UpperCamelCase		,							padding="max_length"		,							max_length=self.tokenizer.model_max_length		,							truncation=_UpperCamelCase		,							return_tensors="pt"		,							)
																	SCREAMING_SNAKE_CASE				=				uncond_input.attention_mask.bool().to(_UpperCamelCase					)
																	SCREAMING_SNAKE_CASE				=				self.text_encoder(uncond_input.input_ids.to(_UpperCamelCase					)					)

																	SCREAMING_SNAKE_CASE				=				uncond_embeddings_text_encoder_output.text_embeds
																	SCREAMING_SNAKE_CASE				=				uncond_embeddings_text_encoder_output.last_hidden_state

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

																	SCREAMING_SNAKE_CASE				=				uncond_embeddings.shape[1]
																	SCREAMING_SNAKE_CASE				=				uncond_embeddings.repeat(1		,							_UpperCamelCase					)
																	SCREAMING_SNAKE_CASE				=				uncond_embeddings.view(batch_size * num_images_per_prompt		,							_UpperCamelCase					)

																	SCREAMING_SNAKE_CASE				=				uncond_text_encoder_hidden_states.shape[1]
																	SCREAMING_SNAKE_CASE				=				uncond_text_encoder_hidden_states.repeat(1		,							_UpperCamelCase		,							1					)
																	SCREAMING_SNAKE_CASE				=				uncond_text_encoder_hidden_states.view(
																	    batch_size * num_images_per_prompt		,							_UpperCamelCase		,							-1					)
																	SCREAMING_SNAKE_CASE				=				uncond_text_mask.repeat_interleave(_UpperCamelCase		,							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
																	SCREAMING_SNAKE_CASE				=				torch.cat([uncond_embeddings, text_embeddings]					)
																	SCREAMING_SNAKE_CASE				=				torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)

																	SCREAMING_SNAKE_CASE				=				torch.cat([uncond_text_mask, text_mask]					)

												return text_embeddings, text_encoder_hidden_states, text_mask




							@property
							def 		__snake_case(  self      :    int					)    -> List[str]:



												'''simple docstring'''

												if self.device != torch.device("meta"					) or not hasattr(self.prior		,							"_hf_hook"					):
																	return self.device
												for module in self.prior.modules():
																	if (
																	    hasattr(_UpperCamelCase		,							"_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




							def 		__snake_case(  self      :    List[Any]		,							_UpperCamelCase      :    Any		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    Any		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    Dict					)    -> int:



												'''simple docstring'''

												if latents is None:
																	SCREAMING_SNAKE_CASE				=				randn_tensor(_UpperCamelCase		,							generator=_UpperCamelCase		,							device=_UpperCamelCase		,							dtype=_UpperCamelCase					)
												else:
																	if latents.shape != shape:
																						raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"					)
																	SCREAMING_SNAKE_CASE				=				latents.to(_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				latents * scheduler.init_noise_sigma
												return latents




							def 		__snake_case(  self      :    Optional[int]		,							_UpperCamelCase      :    Optional[Union[str, torch.device]] = None					)    -> List[Any]:



												'''simple docstring'''

												self.decoder_pipe.to(_UpperCamelCase					)
												super().to(_UpperCamelCase					)





							@torch.no_grad()
							def __call__(  self      :    Optional[Any]		,							_UpperCamelCase      :    Optional[Union[str, List[str]]] = None		,							_UpperCamelCase      :    Optional[int] = None		,							_UpperCamelCase      :    Optional[int] = None		,							_UpperCamelCase      :    int = 1		,							_UpperCamelCase      :    int = 25		,							_UpperCamelCase      :    Optional[torch.Generator] = None		,							_UpperCamelCase      :    Optional[torch.FloatTensor] = None		,							_UpperCamelCase      :    Optional[Union[CLIPTextModelOutput, Tuple]] = None		,							_UpperCamelCase      :    Optional[torch.Tensor] = None		,							_UpperCamelCase      :    float = 4.0		,							_UpperCamelCase      :    float = 8.0		,							_UpperCamelCase      :    int = 50		,							_UpperCamelCase      :    Optional[int] = 1		,							_UpperCamelCase      :    float = 0.0		,							_UpperCamelCase      :    Optional[str] = "pil"		,							_UpperCamelCase      :    bool = True		,							)    -> int:



												'''simple docstring'''

												if prompt is not None:
																	if isinstance(_UpperCamelCase		,							_UpperCamelCase					):
																						SCREAMING_SNAKE_CASE				=				1
																	elif isinstance(_UpperCamelCase		,							_UpperCamelCase					):
																						SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					)
																	else:
																						raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase					)}"					)
												else:
																	SCREAMING_SNAKE_CASE				=				text_model_output[0].shape[0]

												SCREAMING_SNAKE_CASE				=				self._execution_device

												SCREAMING_SNAKE_CASE				=				batch_size * num_images_per_prompt

												SCREAMING_SNAKE_CASE				=				prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

												SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				self._encode_prompt(
												    _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase					)

												# prior

												self.prior_scheduler.set_timesteps(_UpperCamelCase		,							device=_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				self.prior_scheduler.timesteps

												SCREAMING_SNAKE_CASE				=				self.prior.config.embedding_dim

												SCREAMING_SNAKE_CASE				=				self.prepare_latents(
												    (batch_size, embedding_dim)		,							text_embeddings.dtype		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							self.prior_scheduler		,							)

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

																	SCREAMING_SNAKE_CASE				=				self.prior(
																	    _UpperCamelCase		,							timestep=_UpperCamelCase		,							proj_embedding=_UpperCamelCase		,							encoder_hidden_states=_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							).predicted_image_embedding

																	if do_classifier_free_guidance:
																						SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				predicted_image_embedding.chunk(2					)
																						SCREAMING_SNAKE_CASE				=				predicted_image_embedding_uncond + prior_guidance_scale * (
																						    predicted_image_embedding_text - predicted_image_embedding_uncond
																						)

																	if i + 1 == prior_timesteps_tensor.shape[0]:
																						SCREAMING_SNAKE_CASE				=				None
																	else:
																						SCREAMING_SNAKE_CASE				=				prior_timesteps_tensor[i + 1]

																	SCREAMING_SNAKE_CASE				=				self.prior_scheduler.step(
																	    _UpperCamelCase		,							timestep=_UpperCamelCase		,							sample=_UpperCamelCase		,							generator=_UpperCamelCase		,							prev_timestep=_UpperCamelCase		,							).prev_sample

												SCREAMING_SNAKE_CASE				=				self.prior.post_process_latents(_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				prior_latents

												SCREAMING_SNAKE_CASE				=				self.decoder_pipe(
												    image=_UpperCamelCase		,							height=_UpperCamelCase		,							width=_UpperCamelCase		,							num_inference_steps=_UpperCamelCase		,							guidance_scale=_UpperCamelCase		,							generator=_UpperCamelCase		,							output_type=_UpperCamelCase		,							return_dict=_UpperCamelCase		,							num_images_per_prompt=_UpperCamelCase		,							eta=_UpperCamelCase		,							)
												return output





from sklearn.metrics import mean_absolute_error

import datasets


_lowerCamelCase    :      Any	 =						'''\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
'''

_lowerCamelCase    :      str	 =						'''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


_lowerCamelCase    :      List[str]	 =						'''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 "raw_values" : Returns a full set of errors in case of multioutput input.

                 "uniform_average" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''


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




							def 		__snake_case(  self      :    int					)    -> str:



												'''simple docstring'''

												return datasets.MetricInfo(
												    description=_DESCRIPTION		,							citation=_CITATION		,							inputs_description=_KWARGS_DESCRIPTION		,							features=datasets.Features(self._get_feature_types()					)		,							reference_urls=[
												        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"
												    ]		,							)




							def 		__snake_case(  self      :    int					)    -> Tuple:



												'''simple docstring'''

												if self.config_name == "multilist":
																	return {
																	    "predictions": datasets.Sequence(datasets.Value("float"					)					),
																	    "references": datasets.Sequence(datasets.Value("float"					)					),
																	}
												else:
																	return {
																	    "predictions": datasets.Value("float"					),
																	    "references": datasets.Value("float"					),
																	}





							def 		__snake_case(  self      :    Tuple		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    List[Any]		,							_UpperCamelCase      :    str=None		,							_UpperCamelCase      :    Any="uniform_average"					)    -> int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				mean_absolute_error(_UpperCamelCase		,							_UpperCamelCase		,							sample_weight=_UpperCamelCase		,							multioutput=_UpperCamelCase					)

												return {"mae": mae_score}




