




from typing import List, Optional, Union

import torch

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


SCREAMING_SNAKE_CASE:     List[Any]									= logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE:     Tuple									= '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=8 )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		SCREAMING_SNAKE_CASE_					      =							width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor




class    lowercase_		(_snake_case ):



						def __init__(			self	:   Dict					,							snake_case__	:   UNetaDConditionModel					,							snake_case__	:   DDPMScheduler					,							snake_case__	:   VQModel					,							):

								"""simple docstring"""

								super().__init__()

								self.register_modules(
								    unet=__lowerCamelCase					,							scheduler=__lowerCamelCase					,							movq=__lowerCamelCase					,							)
								SCREAMING_SNAKE_CASE_					      =							2 ** (len(self.movq.config.block_out_channels  ) - 1)



						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Optional[int]					,							snake_case__	:   List[Any]					,							snake_case__	:   Optional[Any]					,							snake_case__	:   List[Any]					,							snake_case__	:   Dict					,							snake_case__	:   List[Any]  ):

								"""simple docstring"""

								if latents is None:
										SCREAMING_SNAKE_CASE_					      =							randn_tensor(__lowerCamelCase					,							generator=__lowerCamelCase					,							device=__lowerCamelCase					,							dtype=__lowerCamelCase  )
								else:
										if latents.shape != shape:
												raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''  )
										SCREAMING_SNAKE_CASE_					      =							latents.to(__lowerCamelCase  )

								SCREAMING_SNAKE_CASE_					      =							latents * scheduler.init_noise_sigma
								return latents



						def 	__a					(			self	:   List[Any]					,							snake_case__	:   List[str]=0  ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							torch.device(f'''cuda:{gpu_id}'''  )

								SCREAMING_SNAKE_CASE_					      =							[
								    self.unet,
								    self.movq,
								]
								for cpu_offloaded_model in models:
										if cpu_offloaded_model is not None:
												cpu_offload(__lowerCamelCase					,							__lowerCamelCase  )



						def 	__a					(			self	:   List[str]					,							snake_case__	:   str=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.'  )

								SCREAMING_SNAKE_CASE_					      =							torch.device(f'''cuda:{gpu_id}'''  )

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

								SCREAMING_SNAKE_CASE_					      =							None
								for cpu_offloaded_model in [self.unet, self.movq]:
										SCREAMING_SNAKE_CASE_					      =							cpu_offload_with_hook(__lowerCamelCase					,							__lowerCamelCase					,							prev_module_hook=__lowerCamelCase  )

								# We'll offload the last model manually.
								SCREAMING_SNAKE_CASE_					      =							hook



						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								if not hasattr(self.unet					,							'_hf_hook'  ):
										return self.device
								for module in self.unet.modules():
										if (
										    hasattr(__lowerCamelCase					,							'_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(__lowerCamelCase  )
						def __call__(			self	:   Tuple					,							snake_case__	:   Union[torch.FloatTensor, List[torch.FloatTensor]]					,							snake_case__	:   Union[torch.FloatTensor, List[torch.FloatTensor]]					,							snake_case__	:   int = 5_12					,							snake_case__	:   int = 5_12					,							snake_case__	:   int = 1_00					,							snake_case__	:   float = 4.0					,							snake_case__	:   int = 1					,							snake_case__	:   Optional[Union[torch.Generator, List[torch.Generator]]] = None					,							snake_case__	:   Optional[torch.FloatTensor] = None					,							snake_case__	:   Optional[str] = "pil"					,							snake_case__	:   bool = True					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self._execution_device

								SCREAMING_SNAKE_CASE_					      =							guidance_scale > 1.0

								if isinstance(__lowerCamelCase					,							__lowerCamelCase  ):
										SCREAMING_SNAKE_CASE_					      =							torch.cat(__lowerCamelCase					,							dim=0  )
								SCREAMING_SNAKE_CASE_					      =							image_embeds.shape[0] * num_images_per_prompt
								if isinstance(__lowerCamelCase					,							__lowerCamelCase  ):
										SCREAMING_SNAKE_CASE_					      =							torch.cat(__lowerCamelCase					,							dim=0  )

								if do_classifier_free_guidance:
										SCREAMING_SNAKE_CASE_					      =							image_embeds.repeat_interleave(__lowerCamelCase					,							dim=0  )
										SCREAMING_SNAKE_CASE_					      =							negative_image_embeds.repeat_interleave(__lowerCamelCase					,							dim=0  )

								SCREAMING_SNAKE_CASE_					      =							torch.cat([negative_image_embeds, image_embeds]					,							dim=0  ).to(dtype=self.unet.dtype					,							device=__lowerCamelCase  )

								self.scheduler.set_timesteps(__lowerCamelCase					,							device=__lowerCamelCase  )
								SCREAMING_SNAKE_CASE_					      =							self.scheduler.timesteps

								SCREAMING_SNAKE_CASE_					      =							self.unet.config.in_channels

								SCREAMING_SNAKE_CASE_					      =							downscale_height_and_width(__lowerCamelCase					,							__lowerCamelCase					,							self.movq_scale_factor  )

								# create initial latent
								SCREAMING_SNAKE_CASE_					      =							self.prepare_latents(
								    (batch_size, num_channels_latents, height, width)					,							image_embeds.dtype					,							__lowerCamelCase					,							__lowerCamelCase					,							__lowerCamelCase					,							self.scheduler					,							)

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

										SCREAMING_SNAKE_CASE_					      =							{'''image_embeds''': image_embeds}
										SCREAMING_SNAKE_CASE_					      =							self.unet(
										    sample=__lowerCamelCase					,							timestep=__lowerCamelCase					,							encoder_hidden_states=__lowerCamelCase					,							added_cond_kwargs=__lowerCamelCase					,							return_dict=__lowerCamelCase					,							)[0]

										if do_classifier_free_guidance:
												SCREAMING_SNAKE_CASE_					      =							noise_pred.split(latents.shape[1]					,							dim=1  )
												SCREAMING_SNAKE_CASE_					      =							noise_pred.chunk(2  )
												SCREAMING_SNAKE_CASE_					      =							variance_pred.chunk(2  )
												SCREAMING_SNAKE_CASE_					      =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
												SCREAMING_SNAKE_CASE_					      =							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"]
										):
												SCREAMING_SNAKE_CASE_					      =							noise_pred.split(latents.shape[1]					,							dim=1  )

										# compute the previous noisy sample x_t -> x_t-1
										SCREAMING_SNAKE_CASE_					      =							self.scheduler.step(
										    __lowerCamelCase					,							__lowerCamelCase					,							__lowerCamelCase					,							generator=__lowerCamelCase					,							)[0]
								# post-processing
								SCREAMING_SNAKE_CASE_					      =							self.movq.decode(__lowerCamelCase					,							force_not_quantize=__lowerCamelCase  )['''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"]:
										SCREAMING_SNAKE_CASE_					      =							image * 0.5 + 0.5
										SCREAMING_SNAKE_CASE_					      =							image.clamp(0					,							1  )
										SCREAMING_SNAKE_CASE_					      =							image.cpu().permute(0					,							2					,							3					,							1  ).float().numpy()

								if output_type == "pil":
										SCREAMING_SNAKE_CASE_					      =							self.numpy_to_pil(__lowerCamelCase  )

								if not return_dict:
										return (image,)

								return ImagePipelineOutput(images=__lowerCamelCase  )



'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase			(			unittest.TestCase						):


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:List[str]      =      TFCamembertModel.from_pretrained('''jplu/tf-camembert-base''' )

    UpperCAmelCase__	:Optional[Any]      =      tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]			,       dtype=tf.intaa			,       )  # J'aime le camembert !"

    UpperCAmelCase__	:Any      =      model(__lowerCamelCase )['''last_hidden_state''']
    UpperCAmelCase__	:Dict      =      tf.TensorShape((1, 1_0, 7_6_8) )
    self.assertEqual(output.shape			,       __lowerCamelCase )
    # compare the actual values for a slice.
    UpperCAmelCase__	:Any      =      tf.convert_to_tensor(
        [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]			,       dtype=tf.floataa			,       )
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,       expected_slice.numpy()			,       atol=1e-4 ) )



