
"""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,
)


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

_lowercase											=			'''
    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  _snake_case			(						snake_case__				:       List[Any]		, snake_case__				:       Tuple		, snake_case__				:       Any=8       ):
	A  				=				height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	A  				=				width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor






def  _snake_case			(						snake_case__				:       Union[str, Any]		, snake_case__				:       Dict=512		, snake_case__				:       List[str]=512       ):
	A  				=				pil_image.resize((w, h)		, resample=Image.BICUBIC		, reducing_gap=1       )
	A  				=				np.array(pil_image.convert('RGB'       )       )
	A  				=				arr.astype(np.floataa       ) / 127.5 - 1
	A  				=				np.transpose(snake_case__		, [2, 0, 1]       )
	A  				=				torch.from_numpy(snake_case__       ).unsqueeze(0       )
	return image







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						def __init__(	self       : Union[str, Any]					,A_       : UNetaDConditionModel					,A_       : DDPMScheduler					,A_       : VQModel					,)       ->				Dict:
							super().__init__()

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


						def   _SCREAMING_SNAKE_CASE (	self       : str					,A_       : List[Any]					,A_       : List[Any]					,A_       : List[Any] )       ->				Tuple:
							# get the original timestep using init_timestep
							A  				=				min(int(num_inference_steps * strength )					,A_ )

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

							return timesteps, num_inference_steps - t_start


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : str					,A_       : List[str]					,A_       : str					,A_       : Union[str, Any]					,A_       : Optional[Any]					,A_       : Dict					,A_       : str=None )       ->				Optional[int]:
							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_ )}' )

							A  				=				image.to(device=A_					,dtype=A_ )

							A  				=				batch_size * num_images_per_prompt

							if image.shape[1] == 4:
								A  				=				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_ ):
									A  				=				[
									    self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(A_ )
									]
									A  				=				torch.cat(A_					,dim=0 )
								else:
									A  				=				self.movq.encode(A_ ).latent_dist.sample(A_ )

								A  				=				self.movq.config.scaling_factor * init_latents

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

							A  				=				init_latents.shape
							A  				=				randn_tensor(A_					,generator=A_					,device=A_					,dtype=A_ )

							# get latents
							A  				=				self.scheduler.add_noise(A_					,A_					,A_ )

							A  				=				init_latents

							return latents


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : Dict=0 )       ->				List[str]:
							if is_accelerate_available():
								from accelerate import cpu_offload
							else:
								raise ImportError('Please install accelerate via `pip install accelerate`' )

							A  				=				torch.device(F'cuda:{gpu_id}' )

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


						def   _SCREAMING_SNAKE_CASE (	self       : List[str]					,A_       : List[Any]=0 )       ->				int:
							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.' )

							A  				=				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)

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

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


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				Union[str, Any]:
							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       : int					,A_       : Union[torch.FloatTensor, List[torch.FloatTensor]]					,A_       : Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]					,A_       : Union[torch.FloatTensor, List[torch.FloatTensor]]					,A_       : torch.FloatTensor					,A_       : int = 512					,A_       : int = 512					,A_       : int = 100					,A_       : float = 4.0					,A_       : float = 0.3					,A_       : int = 1					,A_       : Optional[Union[torch.Generator, List[torch.Generator]]] = None					,A_       : Optional[str] = "pil"					,A_       : bool = True					,)       ->				Any:
							A  				=				self._execution_device

							A  				=				guidance_scale > 1.0

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

							A  				=				image_embeds.shape[0]

							if do_classifier_free_guidance:
								A  				=				image_embeds.repeat_interleave(A_					,dim=0 )
								A  				=				negative_image_embeds.repeat_interleave(A_					,dim=0 )
								A  				=				hint.repeat_interleave(A_					,dim=0 )

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

							if not isinstance(A_					,A_ ):
								A  				=				[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' )

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

							A  				=				self.movq.encode(A_ )['latents']
							A  				=				latents.repeat_interleave(A_					,dim=0 )
							self.scheduler.set_timesteps(A_					,device=A_ )
							A				, A  				=				self.get_timesteps(A_					,A_					,A_ )
							A  				=				timesteps[:1].repeat(batch_size * num_images_per_prompt )
							A				, A  				=				downscale_height_and_width(A_					,A_					,self.movq_scale_factor )
							A  				=				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
								A  				=				torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

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

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

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

								A  				=				self.scheduler.step(
								    A_					,A_					,A_					,generator=A_					,)[0]

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

							if output_type == "pil":
								A  				=				self.numpy_to_pil(A_ )

							if not return_dict:
								return (image,)

							return ImagePipelineOutput(images=A_ )

"""simple docstring"""


import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch


_lowercase											=			random.Random()






def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       Union[str, Any]=1.0		, snake_case__				:       int=None		, snake_case__				:       Dict=None       ):
	if rng is None:
		A  				=				global_rng

	A  				=				[]
	for batch_idx in range(shape[0]       ):
		values.append([]       )
		for _ in range(shape[1]       ):
			values[-1].append(rng.random() * scale       )

	return values







@require_torch
class 			lowerCAmelCase_			( unittest.TestCase      ):



						'''simple docstring'''


						def __init__(	self       : int					,A_       : str					,A_       : List[str]=7					,A_       : Tuple=400					,A_       : Union[str, Any]=2000					,A_       : Optional[Any]=1					,A_       : Optional[int]=0.0					,A_       : str=2_4000					,A_       : Optional[int]=True					,)       ->				Dict:
							A  				=				parent
							A  				=				batch_size
							A  				=				min_seq_length
							A  				=				max_seq_length
							A  				=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							A  				=				feature_size
							A  				=				padding_value
							A  				=				sampling_rate
							A  				=				return_attention_mask


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any] )       ->				Optional[int]:
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							}




						def   _SCREAMING_SNAKE_CASE (	self       : Dict					,A_       : List[str]=False					,A_       : str=False )       ->				Optional[Any]:
							def _flatten(A_       : int ):
								return list(itertools.chain(*A_ ) )

							if equal_length:
								A  				=				floats_list((self.batch_size, self.max_seq_length) )
							else:
								# make sure that inputs increase in size
								A  				=				[
								    _flatten(floats_list((x, self.feature_size) ) )
								    for x in range(self.min_seq_length					,self.max_seq_length					,self.seq_length_diff )
								]

							if numpify:
								A  				=				[np.asarray(A_ ) for x in audio_inputs]

							return audio_inputs







@require_torch
class 			lowerCAmelCase_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      Union[str, Any] =      EncodecFeatureExtractor


						def   _SCREAMING_SNAKE_CASE (	self       : List[str] )       ->				List[Any]:
							A  				=				EnCodecFeatureExtractionTester(self )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				str:
							# Tests that all call wrap to encode_plus and batch_encode_plus
							A  				=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
							# create three inputs of length 800, 1000, and 1200
							A  				=				[floats_list((1, x) )[0] for x in range(800					,1400					,200 )]
							A  				=				[np.asarray(A_ ) for audio_input in audio_inputs]

							# Test not batched input
							A  				=				feat_extract(audio_inputs[0]					,return_tensors='np' ).input_values
							A  				=				feat_extract(np_audio_inputs[0]					,return_tensors='np' ).input_values
							self.assertTrue(np.allclose(A_					,A_					,atol=1e-3 ) )

							# Test batched
							A  				=				feat_extract(A_					,padding=A_					,return_tensors='np' ).input_values
							A  				=				feat_extract(A_					,padding=A_					,return_tensors='np' ).input_values
							for enc_seq_a, enc_seq_a in zip(A_					,A_ ):
								self.assertTrue(np.allclose(A_					,A_					,atol=1e-3 ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				Dict:
							A  				=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
							A  				=				np.random.rand(100 ).astype(np.floataa )
							A  				=				np_audio_inputs.tolist()

							for inputs in [py_audio_inputs, np_audio_inputs]:
								A  				=				feature_extractor.pad([{'input_values': inputs}]					,return_tensors='np' )
								self.assertTrue(np_processed.input_values.dtype == np.floataa )
								A  				=				feature_extractor.pad([{'input_values': inputs}]					,return_tensors='pt' )
								self.assertTrue(pt_processed.input_values.dtype == torch.floataa )


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : int )       ->				List[Any]:
							from datasets import load_dataset

							A  				=				load_dataset('hf-internal-testing/librispeech_asr_dummy'					,'clean'					,split='validation' )
							# automatic decoding with librispeech
							A  				=				ds.sort('id' ).select(range(A_ ) )[:num_samples]['audio']

							return [x["array"] for x in audio_samples]


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Optional[int]:
							# fmt: off
							A  				=				torch.tensor(
							    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
							     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
							     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
							     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
							     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
							     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03] )
							# fmt: on
							A  				=				self._load_datasamples(1 )
							A  				=				EncodecFeatureExtractor()
							A  				=				feature_extractor(A_					,return_tensors='pt' ).input_values
							self.assertEquals(input_values.shape					,(1, 1, 9_3680) )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]					,A_					,atol=1e-6 ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				List[Any]:
							# fmt: off
							A  				=				torch.tensor(
							    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
							     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
							     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
							     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
							     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
							     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03] )
							# fmt: on
							A  				=				self._load_datasamples(1 )
							A  				=				[np.tile(input_audio[0][None]					,reps=(2, 1) )]
							input_audio[0][1] *= 0.5
							A  				=				EncodecFeatureExtractor(feature_size=2 )
							A  				=				feature_extractor(A_					,return_tensors='pt' ).input_values
							self.assertEquals(input_values.shape					,(1, 2, 9_3680) )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]					,A_					,atol=1e-6 ) )
							self.assertTrue(torch.allclose(input_values[0, 1, :30]					,EXPECTED_INPUT_VALUES * 0.5					,atol=1e-6 ) )




						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				List[str]:
							A  				=				self._load_datasamples(2 )
							# would be easier if the stride was like
							A  				=				EncodecFeatureExtractor(feature_size=1					,chunk_length_s=1					,overlap=0.01 )

							# pad and trunc raise an error ?
							with self.assertRaisesRegex(
							    A_					,'^Both padding and truncation were set. Make sure you only set one.$'					,):
								A  				=				feature_extractor(
								    A_					,padding='max_length'					,truncation=A_					,return_tensors='pt' ).input_values

							# truncate to chunk
							A  				=				feature_extractor(A_					,truncation=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 7_1520) )  # 2 chunks

							# force truncate to max_length
							A  				=				feature_extractor(
							    A_					,truncation=A_					,max_length=4_8000					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 4_8000) )

							# pad to chunk
							A  				=				feature_extractor(A_					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(padded_outputs.shape					,(2, 1, 9_5280) )

							# pad to chunk
							A  				=				feature_extractor(A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 9_5280) )

							# force pad to max length
							A  				=				feature_extractor(
							    A_					,padding='max_length'					,max_length=10_0000					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 10_0000) )

							# force no pad
							with self.assertRaisesRegex(
							    A_					,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'					,):
								A  				=				feature_extractor(A_					,padding=A_					,return_tensors='pt' ).input_values

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )

							# no pad if no chunk_length_s
							A  				=				None
							with self.assertRaisesRegex(
							    A_					,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'					,):
								A  				=				feature_extractor(A_					,padding=A_					,return_tensors='pt' ).input_values

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )

							# no pad if no overlap
							A  				=				2
							A  				=				None
							with self.assertRaisesRegex(
							    A_					,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'					,):
								A  				=				feature_extractor(A_					,padding=A_					,return_tensors='pt' ).input_values

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )