




"""simple docstring"""




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

lowerCAmelCase							:			str     							=							"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""



def     a__ (				snake_case__       , snake_case__       , snake_case__=8				)						->					Optional[Any]:
			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     a__ (				snake_case__       , snake_case__=5_12       , snake_case__=5_12				)						->					int:
			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(snake_case__       , [2, 0, 1]				)
			lowerCamelCase			      =					torch.from_numpy(snake_case__				).unsqueeze(0				)
			return image






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a		, ):





							"""simple docstring"""
							super().__init__()

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





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							# 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    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a=None ):





							"""simple docstring"""
							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    _lowerCAmelCase				(       self		, _a=0 ):





							"""simple docstring"""
							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    _lowerCAmelCase				(       self		, _a=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.""" )

							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    _lowerCAmelCase				(       self ):





							"""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		, _a		, _a		, _a		, _a = 512		, _a = 512		, _a = 100		, _a = 4.0		, _a = 0.3		, _a = 1		, _a = None		, _a = "pil"		, _a = True		, ):





							"""simple docstring"""
							lowerCamelCase			      =					self._execution_device

							lowerCamelCase			      =					guidance_scale > 1.0

							if isinstance(_a		, _a ):
										lowerCamelCase			      =					torch.cat(_a		, dim=0 )
							lowerCamelCase			      =					image_embeds.shape[0]
							if isinstance(_a		, _a ):
										lowerCamelCase			      =					torch.cat(_a		, dim=0 )

							if do_classifier_free_guidance:
										lowerCamelCase			      =					image_embeds.repeat_interleave(_a		, dim=0 )
										lowerCamelCase			      =					negative_image_embeds.repeat_interleave(_a		, dim=0 )

							lowerCamelCase			      =					torch.cat([negative_image_embeds, image_embeds]		, 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}
										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 )









"""simple docstring"""




import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roformer.modeling_flax_roformer import (
					    FlaxRoFormerForMaskedLM,
					    FlaxRoFormerForMultipleChoice,
					    FlaxRoFormerForQuestionAnswering,
					    FlaxRoFormerForSequenceClassification,
					    FlaxRoFormerForTokenClassification,
					    FlaxRoFormerModel,
					)






class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a=13		, _a=7		, _a=True		, _a=True		, _a=True		, _a=True		, _a=99		, _a=32		, _a=5		, _a=4		, _a=37		, _a="gelu"		, _a=0.1		, _a=0.1		, _a=512		, _a=16		, _a=2		, _a=0.02		, _a=4		, ):





							"""simple docstring"""
							lowerCamelCase			      =					parent
							lowerCamelCase			      =					batch_size
							lowerCamelCase			      =					seq_length
							lowerCamelCase			      =					is_training
							lowerCamelCase			      =					use_attention_mask
							lowerCamelCase			      =					use_token_type_ids
							lowerCamelCase			      =					use_labels
							lowerCamelCase			      =					vocab_size
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					hidden_dropout_prob
							lowerCamelCase			      =					attention_probs_dropout_prob
							lowerCamelCase			      =					max_position_embeddings
							lowerCamelCase			      =					type_vocab_size
							lowerCamelCase			      =					type_sequence_label_size
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					num_choices





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size )

							lowerCamelCase			      =					None
							if self.use_attention_mask:
										lowerCamelCase			      =					random_attention_mask([self.batch_size, self.seq_length] )

							lowerCamelCase			      =					None
							if self.use_token_type_ids:
										lowerCamelCase			      =					ids_tensor([self.batch_size, self.seq_length]		, self.type_vocab_size )

							lowerCamelCase			      =					RoFormerConfig(
							    vocab_size=self.vocab_size		, hidden_size=self.hidden_size		, num_hidden_layers=self.num_hidden_layers		, num_attention_heads=self.num_attention_heads		, intermediate_size=self.intermediate_size		, hidden_act=self.hidden_act		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, max_position_embeddings=self.max_position_embeddings		, type_vocab_size=self.type_vocab_size		, is_decoder=_a		, initializer_range=self.initializer_range		, )

							return config, input_ids, token_type_ids, attention_mask





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.prepare_config_and_inputs()
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					config_and_inputs
							lowerCamelCase			      =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
							return config, inputs_dict






@require_flax
class     __magic_name__							(       UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						True

				__UpperCamelCase			  =						(
				    (
				        FlaxRoFormerModel,
				        FlaxRoFormerForMaskedLM,
				        FlaxRoFormerForSequenceClassification,
				        FlaxRoFormerForTokenClassification,
				        FlaxRoFormerForMultipleChoice,
				        FlaxRoFormerForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					FlaxRoFormerModelTester(self )





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							for model_class_name in self.all_model_classes:
										lowerCamelCase			      =					model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""		, from_pt=_a )
										lowerCamelCase			      =					model(np.ones((1, 1) ) )
										self.assertIsNotNone(_a )






@require_flax
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
							lowerCamelCase			      =					jnp.array([[0, 1, 2, 3, 4, 5]] )
							lowerCamelCase			      =					model(_a )[0]

							lowerCamelCase			      =					50_000

							lowerCamelCase			      =					(1, 6, vocab_size)
							self.assertEqual(output.shape		, _a )

							lowerCamelCase			      =					jnp.array(
							    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )

							self.assertTrue(jnp.allclose(output[:, :3, :3]		, _a		, atol=1e-4 ) )



