from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase						=		logging.get_logger(__name__)





class  _a							(    _lowercase):
	_a  :  int				   =							['''input_values''', '''padding_mask''']




	def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		int = 1		, _SCREAMING_SNAKE_CASE						:		int = 2_4000		, _SCREAMING_SNAKE_CASE						:		float = 0.0		, _SCREAMING_SNAKE_CASE						:		float = None		, _SCREAMING_SNAKE_CASE						:		float = None		, **_SCREAMING_SNAKE_CASE						:		str		, )->	Dict:
			super().__init__(feature_size=_SCREAMING_SNAKE_CASE		, sampling_rate=_SCREAMING_SNAKE_CASE		, padding_value=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Dict   					= chunk_length_s
			lowerCAmelCase__       :      int   					= overlap




	@property
	def 							UpperCAmelCase__( self						:		int		)->	Optional[int]:
			if self.chunk_length_s is None:
					return None
			else:
					return int(self.chunk_length_s * self.sampling_rate		)




	@property
	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Optional[int]:
			if self.chunk_length_s is None or self.overlap is None:
					return None
			else:
					return max(1		, int((1.0 - self.overlap) * self.chunk_length		)		)




	def __call__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		, _SCREAMING_SNAKE_CASE						:		Optional[Union[bool, str, PaddingStrategy]] = None		, _SCREAMING_SNAKE_CASE						:		Optional[bool] = False		, _SCREAMING_SNAKE_CASE						:		Optional[int] = None		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, TensorType]] = None		, _SCREAMING_SNAKE_CASE						:		Optional[int] = None		, )->	BatchFeature:
			if sampling_rate is not None:
					if sampling_rate != self.sampling_rate:
							raise ValueError(
							    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
							    F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
							    F' {self.sampling_rate} and not {sampling_rate}.'		)
			else:
					logger.warning(
					    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
					    '''Failing to do so can result in silent errors that might be hard to debug.'''		)

			if padding and truncation:
					raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''		)
			elif padding is None:
					# by default let's pad the inputs
					lowerCAmelCase__       :      Union[str, Any]   					= True

			lowerCAmelCase__       :      Any   					= bool(
			    isinstance(_SCREAMING_SNAKE_CASE		, (list, tuple)		) and (isinstance(raw_audio[0]		, (np.ndarray, tuple, list)		))		)

			if is_batched:
					lowerCAmelCase__       :      str   					= [np.asarray(_SCREAMING_SNAKE_CASE		, dtype=np.floataa		).T for audio in raw_audio]
			elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE		, np.ndarray		):
					lowerCAmelCase__       :      str   					= np.asarray(_SCREAMING_SNAKE_CASE		, dtype=np.floataa		)
			elif isinstance(_SCREAMING_SNAKE_CASE		, np.ndarray		) and raw_audio.dtype is np.dtype(np.floataa		):
					lowerCAmelCase__       :      Any   					= raw_audio.astype(np.floataa		)

			# always return batch
			if not is_batched:
					lowerCAmelCase__       :      Any   					= [np.asarray(_SCREAMING_SNAKE_CASE		).T]

			# verify inputs are valid
			for idx, example in enumerate(_SCREAMING_SNAKE_CASE		):
					if example.ndim > 2:
							raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'		)
					if self.feature_size == 1 and example.ndim != 1:
							raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'		)
					if self.feature_size == 2 and example.shape[-1] != 2:
							raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'		)

			lowerCAmelCase__       :      Any   					= None
			lowerCAmelCase__       :      Dict   					= BatchFeature({'''input_values''': raw_audio}		)
			if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
					if truncation:
							lowerCAmelCase__       :      str   					= min(array.shape[0] for array in raw_audio		)
							lowerCAmelCase__       :      Optional[Any]   					= int(np.floor(max_length / self.chunk_stride		)		)
							lowerCAmelCase__       :      Any   					= (nb_step - 1) * self.chunk_stride + self.chunk_length
					elif padding:
							lowerCAmelCase__       :      Union[str, Any]   					= max(array.shape[0] for array in raw_audio		)
							lowerCAmelCase__       :      Optional[Any]   					= int(np.ceil(max_length / self.chunk_stride		)		)
							lowerCAmelCase__       :      Any   					= (nb_step - 1) * self.chunk_stride + self.chunk_length
							lowerCAmelCase__       :      str   					= '''max_length'''
					else:
							lowerCAmelCase__       :      Tuple   					= input_values

        # normal padding on batch
			if padded_inputs is None:
					lowerCAmelCase__       :      Union[str, Any]   					= self.pad(
					    _SCREAMING_SNAKE_CASE		, max_length=_SCREAMING_SNAKE_CASE		, truncation=_SCREAMING_SNAKE_CASE		, padding=_SCREAMING_SNAKE_CASE		, return_attention_mask=_SCREAMING_SNAKE_CASE		, )
					if padding:
							lowerCAmelCase__       :      Optional[Any]   					= padded_inputs.pop('''attention_mask'''		)

			lowerCAmelCase__       :      Union[str, Any]   					= []
			for example in padded_inputs.pop('''input_values'''		):
					if self.feature_size == 1:
							lowerCAmelCase__       :      Optional[int]   					= example[..., None]
					input_values.append(example.T		)

			lowerCAmelCase__       :      Dict   					= input_values
			if return_tensors is not None:
					lowerCAmelCase__       :      Any   					= padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE		)

			return padded_inputs








import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCamelCase						=		False





class  _a							(    unittest.TestCase):
	pass





@nightly
@require_torch_gpu
class  _a							(    unittest.TestCase):




	def 							UpperCAmelCase__( self						:		int		)->	int:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def 							UpperCAmelCase__( self						:		Any		)->	Dict:
			lowerCAmelCase__       :      int   					= VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''		, torch_dtype=torch.floataa		)
			pipe.to(_SCREAMING_SNAKE_CASE		)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      List[str]   					= load_image(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

			lowerCAmelCase__       :      List[str]   					= torch.manual_seed(0		)
			lowerCAmelCase__       :      List[str]   					= pipe.dual_guided(
			    prompt='''first prompt'''		, image=_SCREAMING_SNAKE_CASE		, text_to_image_strength=0.75		, generator=_SCREAMING_SNAKE_CASE		, guidance_scale=7.5		, num_inference_steps=2		, output_type='''numpy'''		, ).images

			with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__       :      List[Any]   					= VersatileDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE		, torch_dtype=torch.floataa		)
			pipe.to(_SCREAMING_SNAKE_CASE		)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      List[str]   					= generator.manual_seed(0		)
			lowerCAmelCase__       :      str   					= pipe.dual_guided(
			    prompt='''first prompt'''		, image=_SCREAMING_SNAKE_CASE		, text_to_image_strength=0.75		, generator=_SCREAMING_SNAKE_CASE		, guidance_scale=7.5		, num_inference_steps=2		, output_type='''numpy'''		, ).images

			assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"




	def 							UpperCAmelCase__( self						:		Tuple		)->	List[Any]:
			lowerCAmelCase__       :      Optional[int]   					= VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''		, torch_dtype=torch.floataa		)
			pipe.to(_SCREAMING_SNAKE_CASE		)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      int   					= '''cyberpunk 2077'''
			lowerCAmelCase__       :      List[str]   					= load_image(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
			lowerCAmelCase__       :      List[Any]   					= torch.manual_seed(0		)
			lowerCAmelCase__       :      Union[str, Any]   					= pipe.dual_guided(
			    prompt=_SCREAMING_SNAKE_CASE		, image=_SCREAMING_SNAKE_CASE		, text_to_image_strength=0.75		, generator=_SCREAMING_SNAKE_CASE		, guidance_scale=7.5		, num_inference_steps=50		, output_type='''numpy'''		, ).images

			lowerCAmelCase__       :      List[Any]   					= image[0, 253:256, 253:256, -1]

			assert image.shape == (1, 512, 512, 3)
			lowerCAmelCase__       :      List[str]   					= np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]		)

			assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1

			lowerCAmelCase__       :      int   					= '''A painting of a squirrel eating a burger '''
			lowerCAmelCase__       :      Optional[Any]   					= torch.manual_seed(0		)
			lowerCAmelCase__       :      Union[str, Any]   					= pipe.text_to_image(
			    prompt=_SCREAMING_SNAKE_CASE		, generator=_SCREAMING_SNAKE_CASE		, guidance_scale=7.5		, num_inference_steps=50		, output_type='''numpy'''		).images

			lowerCAmelCase__       :      List[Any]   					= image[0, 253:256, 253:256, -1]

			assert image.shape == (1, 512, 512, 3)
			lowerCAmelCase__       :      Optional[Any]   					= np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]		)

			assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1

			lowerCAmelCase__       :      Optional[Any]   					= pipe.image_variation(_SCREAMING_SNAKE_CASE		, generator=_SCREAMING_SNAKE_CASE		, output_type='''numpy'''		).images

			lowerCAmelCase__       :      List[Any]   					= image[0, 253:256, 253:256, -1]

			assert image.shape == (1, 512, 512, 3)
			lowerCAmelCase__       :      Any   					= np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]		)

			assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1




