

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


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

lowerCamelCase   					=  '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=8   ):

					"""simple docstring"""






					__lowerCAmelCase       =				height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__lowerCAmelCase       =				width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__=512		,       UpperCAmelCase__=512   ):

					"""simple docstring"""






					__lowerCAmelCase       =				pil_image.resize((w, h)		,       resample=Image.BICUBIC		,       reducing_gap=1   )
					__lowerCAmelCase       =				np.array(pil_image.convert('RGB'   )   )
					__lowerCAmelCase       =				arr.astype(np.floataa   ) / 127.5 - 1
					__lowerCAmelCase       =				np.transpose(UpperCAmelCase__		,       [2, 0, 1]   )
					__lowerCAmelCase       =				torch.from_numpy(UpperCAmelCase__   ).unsqueeze(0   )
					return image





class      snake_case_      (					_a				):


							"""simple docstring"""





							def __init__(			self       ,    _A       ,    _A       ,    _A       ,    ):
												super().__init__()

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




							def 							A__	(			self       ,    _A       ,    _A       ,    _A				):
												# get the original timestep using init_timestep
												__lowerCAmelCase       =				min(int(num_inference_steps * strength				)       ,    _A				)

												__lowerCAmelCase       =				max(num_inference_steps - init_timestep       ,    0				)
												__lowerCAmelCase       =				self.scheduler.timesteps[t_start:]

												return timesteps, num_inference_steps - t_start




							def 							A__	(			self       ,    _A       ,    _A       ,    _A       ,    _A       ,    _A       ,    _A       ,    _A=None				):
												if not isinstance(_A       ,    (torch.Tensor, PIL.Image.Image, list)				):
																	raise ValueError(
																	    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A				)}"""				)

												__lowerCAmelCase       =				image.to(device=_A       ,    dtype=_A				)

												__lowerCAmelCase       =				batch_size * num_images_per_prompt

												if image.shape[1] == 4:
																	__lowerCAmelCase       =				image

												else:
																	if isinstance(_A       ,    _A				) and len(_A				) != batch_size:
																						raise ValueError(
																						    F"""You have passed a list of generators of length {len(_A				)}, but requested an effective batch"""
																						    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

																	elif isinstance(_A       ,    _A				):
																						__lowerCAmelCase       =				[
																						    self.movq.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(_A				)
																						]
																						__lowerCAmelCase       =				torch.cat(_A       ,    dim=0				)
																	else:
																						__lowerCAmelCase       =				self.movq.encode(_A				).latent_dist.sample(_A				)

																	__lowerCAmelCase       =				self.movq.config.scaling_factor * init_latents

												__lowerCAmelCase       =				torch.cat([init_latents]       ,    dim=0				)

												__lowerCAmelCase       =				init_latents.shape
												__lowerCAmelCase       =				randn_tensor(_A       ,    generator=_A       ,    device=_A       ,    dtype=_A				)

												# get latents
												__lowerCAmelCase       =				self.scheduler.add_noise(_A       ,    _A       ,    _A				)

												__lowerCAmelCase       =				init_latents

												return latents




							def 							A__	(			self       ,    _A=0				):
												if is_accelerate_available():
																	from accelerate import cpu_offload
												else:
																	raise ImportError('Please install accelerate via `pip install accelerate`'				)

												__lowerCAmelCase       =				torch.device(F"""cuda:{gpu_id}"""				)

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




							def 							A__	(			self       ,    _A=0				):
												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.'				)

												__lowerCAmelCase       =				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)

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

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




							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def 							A__	(			self				):
												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       ,    _A       ,    _A       ,    _A       ,    _A       ,    _A = 5_1_2       ,    _A = 5_1_2       ,    _A = 1_0_0       ,    _A = 4.0       ,    _A = 0.3       ,    _A = 1       ,    _A = None       ,    _A = "pil"       ,    _A = True       ,    ):
												__lowerCAmelCase       =				self._execution_device

												__lowerCAmelCase       =				guidance_scale > 1.0

												if isinstance(_A       ,    _A				):
																	__lowerCAmelCase       =				torch.cat(_A       ,    dim=0				)
												if isinstance(_A       ,    _A				):
																	__lowerCAmelCase       =				torch.cat(_A       ,    dim=0				)
												if isinstance(_A       ,    _A				):
																	__lowerCAmelCase       =				torch.cat(_A       ,    dim=0				)

												__lowerCAmelCase       =				image_embeds.shape[0]

												if do_classifier_free_guidance:
																	__lowerCAmelCase       =				image_embeds.repeat_interleave(_A       ,    dim=0				)
																	__lowerCAmelCase       =				negative_image_embeds.repeat_interleave(_A       ,    dim=0				)
																	__lowerCAmelCase       =				hint.repeat_interleave(_A       ,    dim=0				)

												__lowerCAmelCase       =				torch.cat([negative_image_embeds, image_embeds]       ,    dim=0				).to(dtype=self.unet.dtype       ,    device=_A				)
												__lowerCAmelCase       =				torch.cat([hint, hint]       ,    dim=0				).to(dtype=self.unet.dtype       ,    device=_A				)

												if not isinstance(_A       ,    _A				):
																	__lowerCAmelCase       =				[image]
												if not all(isinstance(_A       ,    (PIL.Image.Image, torch.Tensor)				) for i in image				):
																	raise ValueError(
																	    F"""Input is in incorrect format: {[type(_A				) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""				)

												__lowerCAmelCase       =				torch.cat([prepare_image(_A       ,    _A       ,    _A				) for i in image]       ,    dim=0				)
												__lowerCAmelCase       =				image.to(dtype=image_embeds.dtype       ,    device=_A				)

												__lowerCAmelCase       =				self.movq.encode(_A				)['latents']
												__lowerCAmelCase       =				latents.repeat_interleave(_A       ,    dim=0				)
												self.scheduler.set_timesteps(_A       ,    device=_A				)
												__lowerCAmelCase,						__lowerCAmelCase       =				self.get_timesteps(_A       ,    _A       ,    _A				)
												__lowerCAmelCase       =				timesteps[:1].repeat(batch_size * num_images_per_prompt				)
												__lowerCAmelCase,						__lowerCAmelCase       =				downscale_height_and_width(_A       ,    _A       ,    self.movq_scale_factor				)
												__lowerCAmelCase       =				self.prepare_latents(
												    _A       ,    _A       ,    _A       ,    _A       ,    image_embeds.dtype       ,    _A       ,    _A				)
												for i, t in enumerate(self.progress_bar(_A				)				):
																	# expand the latents if we are doing classifier free guidance
																	__lowerCAmelCase       =				torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

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

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

																	# compute the previous noisy sample x_t -> x_t-1

																	__lowerCAmelCase       =				self.scheduler.step(
																	    _A       ,    _A       ,    _A       ,    generator=_A       ,    )[0]

												# post-processing
												__lowerCAmelCase       =				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"]:
																	__lowerCAmelCase       =				image * 0.5 + 0.5
																	__lowerCAmelCase       =				image.clamp(0       ,    1				)
																	__lowerCAmelCase       =				image.cpu().permute(0       ,    2       ,    3       ,    1				).float().numpy()

												if output_type == "pil":
																	__lowerCAmelCase       =				self.numpy_to_pil(_A				)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=_A				)









import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase   					=  logging.get_logger(__name__)

lowerCamelCase   					=  {}





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""fsmt_decoder"""




							def __init__(			self       ,    _A=0       ,    _A=0				):
												super().__init__()
												__lowerCAmelCase       =				vocab_size
												__lowerCAmelCase       =				bos_token_id





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""fsmt"""
							__UpperCAmelCase      ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




							def __init__(			self       ,    _A=["en", "de"]       ,    _A=4_2_0_2_4       ,    _A=4_2_0_2_4       ,    _A="relu"       ,    _A=1_0_2_4       ,    _A=2_0_0       ,    _A=1_0_2_4       ,    _A=4_0_9_6       ,    _A=1_2       ,    _A=1_6       ,    _A=0.0       ,    _A=4_0_9_6       ,    _A=1_2       ,    _A=1_6       ,    _A=0.0       ,    _A=0.0       ,    _A=0.1       ,    _A=0.0       ,    _A=0.02       ,    _A=2       ,    _A=True       ,    _A=True       ,    _A=False       ,    _A=5       ,    _A=1.0       ,    _A=False       ,    _A=True       ,    _A=1       ,    _A=0       ,    _A=2       ,    _A=2       ,    **_A       ,    ):
												__lowerCAmelCase       =				langs
												__lowerCAmelCase       =				src_vocab_size
												__lowerCAmelCase       =				tgt_vocab_size
												__lowerCAmelCase       =				d_model  # encoder_embed_dim and decoder_embed_dim

												__lowerCAmelCase       =				encoder_ffn_dim
												__lowerCAmelCase       =				__lowerCAmelCase       =				encoder_layers
												__lowerCAmelCase       =				encoder_attention_heads
												__lowerCAmelCase       =				encoder_layerdrop
												__lowerCAmelCase       =				decoder_layerdrop
												__lowerCAmelCase       =				decoder_ffn_dim
												__lowerCAmelCase       =				decoder_layers
												__lowerCAmelCase       =				decoder_attention_heads
												__lowerCAmelCase       =				max_position_embeddings
												__lowerCAmelCase       =				init_std  # Normal(0, this parameter)
												__lowerCAmelCase       =				activation_function

												__lowerCAmelCase       =				DecoderConfig(vocab_size=_A       ,    bos_token_id=_A				)
												if "decoder" in common_kwargs:
																	del common_kwargs["decoder"]

												__lowerCAmelCase       =				scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												__lowerCAmelCase       =				attention_dropout
												__lowerCAmelCase       =				activation_dropout
												__lowerCAmelCase       =				dropout

												__lowerCAmelCase       =				use_cache
												super().__init__(
												    pad_token_id=_A       ,    bos_token_id=_A       ,    eos_token_id=_A       ,    decoder_start_token_id=_A       ,    is_encoder_decoder=_A       ,    tie_word_embeddings=_A       ,    forced_eos_token_id=_A       ,    max_length=_A       ,    num_beams=_A       ,    length_penalty=_A       ,    early_stopping=_A       ,    **_A       ,    )




							def 							A__	(			self				):
												__lowerCAmelCase       =				copy.deepcopy(self.__dict__				)
												__lowerCAmelCase       =				self.decoder.to_dict()
												__lowerCAmelCase       =				self.__class__.model_type
												return output






