
import warnings
from typing import List, Optional, Union

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

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					CONFIG_NAME



							@register_to_config
							def __init__(					self       ,		SCREAMING_SNAKE_CASE_ = True       ,		SCREAMING_SNAKE_CASE_ = 8       ,		SCREAMING_SNAKE_CASE_ = "lanczos"       ,		SCREAMING_SNAKE_CASE_ = True       ,		SCREAMING_SNAKE_CASE_ = False       ,		)->				Union[str, Any]:





													'''simple docstring'''






													super().__init__()



							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_     )->				PIL.Image.Image:





													'''simple docstring'''






													if images.ndim == 3:
																			__UpperCamelCase       					=     images[None, ...]
													__UpperCamelCase       					=     (images * 255).round().astype('''uint8'''     )
													if images.shape[-1] == 1:
																			# special case for grayscale (single channel) images
																			__UpperCamelCase       					=     [Image.fromarray(image.squeeze()       ,		mode='''L'''     ) for image in images]
													else:
																			__UpperCamelCase       					=     [Image.fromarray(SCREAMING_SNAKE_CASE_     ) for image in images]

													return pil_images



							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_     )->				np.ndarray:





													'''simple docstring'''






													if not isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ):
																			__UpperCamelCase       					=     [images]
													__UpperCamelCase       					=     [np.array(SCREAMING_SNAKE_CASE_     ).astype(np.floataa     ) / 2_5_5.0 for image in images]
													__UpperCamelCase       					=     np.stack(SCREAMING_SNAKE_CASE_       ,		axis=0     )

													return images



							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_     )->				torch.FloatTensor:





													'''simple docstring'''






													if images.ndim == 3:
																			__UpperCamelCase       					=     images[..., None]

													__UpperCamelCase       					=     torch.from_numpy(images.transpose(0       ,		3       ,		1       ,		2     )     )
													return images



							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_     )->				np.ndarray:





													'''simple docstring'''






													__UpperCamelCase       					=     images.cpu().permute(0       ,		2       ,		3       ,		1     ).float().numpy()
													return images



							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													return 2.0 * images - 1.0



							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_     )->				str:





													'''simple docstring'''






													return (images / 2 + 0.5).clamp(0       ,		1     )



							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_     )->				PIL.Image.Image:





													'''simple docstring'''






													__UpperCamelCase       					=     image.convert('''RGB'''     )
													return image



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		)->				PIL.Image.Image:





													'''simple docstring'''






													if height is None:
																			__UpperCamelCase       					=     image.height
													if width is None:
																			__UpperCamelCase       					=     image.width

													__UpperCamelCase					,							__UpperCamelCase       					=     (
													    x - x % self.config.vae_scale_factor for x in (width, height)
													)  # resize to integer multiple of vae_scale_factor
													__UpperCamelCase       					=     image.resize((width, height)       ,		resample=PIL_INTERPOLATION[self.config.resample]     )
													return image



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		)->				torch.Tensor:





													'''simple docstring'''






													__UpperCamelCase       					=     (PIL.Image.Image, np.ndarray, torch.Tensor)
													if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ):
																			__UpperCamelCase       					=     [image]
													elif not (isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ) and all(isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ) for i in image     )):
																			raise ValueError(
																			    F"Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE_     ) for i in image]}. Currently, we only support {', '.join(SCREAMING_SNAKE_CASE_     )}"     )

													if isinstance(image[0]       ,		PIL.Image.Image     ):
																			if self.config.do_convert_rgb:
																									__UpperCamelCase       					=     [self.convert_to_rgb(SCREAMING_SNAKE_CASE_     ) for i in image]
																			if self.config.do_resize:
																									__UpperCamelCase       					=     [self.resize(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ) for i in image]
																			__UpperCamelCase       					=     self.pil_to_numpy(SCREAMING_SNAKE_CASE_     )  # to np
																			__UpperCamelCase       					=     self.numpy_to_pt(SCREAMING_SNAKE_CASE_     )  # to pt

													elif isinstance(image[0]       ,		np.ndarray     ):
																			__UpperCamelCase       					=     np.concatenate(SCREAMING_SNAKE_CASE_       ,		axis=0     ) if image[0].ndim == 4 else np.stack(SCREAMING_SNAKE_CASE_       ,		axis=0     )
																			__UpperCamelCase       					=     self.numpy_to_pt(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase       					=     image.shape
																			if self.config.do_resize and (
																			    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																			):
																									raise ValueError(
																									    F"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
																									    F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"     )

													elif isinstance(image[0]       ,		torch.Tensor     ):
																			__UpperCamelCase       					=     torch.cat(SCREAMING_SNAKE_CASE_       ,		axis=0     ) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE_       ,		axis=0     )
																			__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase       					=     image.shape

																			# don't need any preprocess if the image is latents
																			if channel == 4:
																									return image

																			if self.config.do_resize and (
																			    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																			):
																									raise ValueError(
																									    F"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
																									    F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"     )

        # expected range [0,1], normalize to [-1,1]
													__UpperCamelCase       					=     self.config.do_normalize
													if image.min() < 0:
																			warnings.warn(
																			    '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
																			    F"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"       ,		SCREAMING_SNAKE_CASE_       ,		)
																			__UpperCamelCase       					=     False

													if do_normalize:
																			__UpperCamelCase       					=     self.normalize(SCREAMING_SNAKE_CASE_     )

													return image



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = "pil"       ,		SCREAMING_SNAKE_CASE_ = None       ,		)->				Optional[Any]:





													'''simple docstring'''






													if not isinstance(SCREAMING_SNAKE_CASE_       ,		torch.Tensor     ):
																			raise ValueError(
																			    F"Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE_     )}. We only support pytorch tensor"     )
													if output_type not in ["latent", "pt", "np", "pil"]:
																			__UpperCamelCase       					=     (
																			    F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
																			    '''`pil`, `np`, `pt`, `latent`'''
																			)
																			deprecate('''Unsupported output_type'''       ,		'''1.0.0'''       ,		SCREAMING_SNAKE_CASE_       ,		standard_warn=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     '''np'''

													if output_type == "latent":
																			return image

													if do_denormalize is None:
																			__UpperCamelCase       					=     [self.config.do_normalize] * image.shape[0]

													__UpperCamelCase       					=     torch.stack(
													    [self.denormalize(image[i]     ) if do_denormalize[i] else image[i] for i in range(image.shape[0]     )]     )

													if output_type == "pt":
																			return image

													__UpperCamelCase       					=     self.pt_to_numpy(SCREAMING_SNAKE_CASE_     )

													if output_type == "np":
																			return image

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






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					CONFIG_NAME



							@register_to_config
							def __init__(					self       ,		SCREAMING_SNAKE_CASE_ = True       ,		SCREAMING_SNAKE_CASE_ = 8       ,		SCREAMING_SNAKE_CASE_ = "lanczos"       ,		SCREAMING_SNAKE_CASE_ = True       ,		)->				Optional[Any]:





													'''simple docstring'''






													super().__init__()



							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													if images.ndim == 3:
																			__UpperCamelCase       					=     images[None, ...]
													__UpperCamelCase       					=     (images * 255).round().astype('''uint8'''     )
													if images.shape[-1] == 1:
																			# special case for grayscale (single channel) images
																			__UpperCamelCase       					=     [Image.fromarray(image.squeeze()       ,		mode='''L'''     ) for image in images]
													else:
																			__UpperCamelCase       					=     [Image.fromarray(image[:, :, :3]     ) for image in images]

													return pil_images



							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_     )->				str:





													'''simple docstring'''






													return image[:, :, 1] * 2**8 + image[:, :, 2]



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													if images.ndim == 3:
																			__UpperCamelCase       					=     images[None, ...]
													__UpperCamelCase       					=     images[:, :, :, 3:]
													if images.shape[-1] == 6:
																			__UpperCamelCase       					=     (images_depth * 255).round().astype('''uint8'''     )
																			__UpperCamelCase       					=     [
																			    Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE_     )       ,		mode='''I;16'''     ) for image_depth in images_depth
																			]
													elif images.shape[-1] == 4:
																			__UpperCamelCase       					=     (images_depth * 6_5_5_3_5.0).astype(np.uintaa     )
																			__UpperCamelCase       					=     [Image.fromarray(SCREAMING_SNAKE_CASE_       ,		mode='''I;16'''     ) for image_depth in images_depth]
													else:
																			raise Exception('''Not supported'''     )

													return pil_images



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = "pil"       ,		SCREAMING_SNAKE_CASE_ = None       ,		)->				Any:





													'''simple docstring'''






													if not isinstance(SCREAMING_SNAKE_CASE_       ,		torch.Tensor     ):
																			raise ValueError(
																			    F"Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE_     )}. We only support pytorch tensor"     )
													if output_type not in ["latent", "pt", "np", "pil"]:
																			__UpperCamelCase       					=     (
																			    F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
																			    '''`pil`, `np`, `pt`, `latent`'''
																			)
																			deprecate('''Unsupported output_type'''       ,		'''1.0.0'''       ,		SCREAMING_SNAKE_CASE_       ,		standard_warn=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     '''np'''

													if do_denormalize is None:
																			__UpperCamelCase       					=     [self.config.do_normalize] * image.shape[0]

													__UpperCamelCase       					=     torch.stack(
													    [self.denormalize(image[i]     ) if do_denormalize[i] else image[i] for i in range(image.shape[0]     )]     )

													__UpperCamelCase       					=     self.pt_to_numpy(SCREAMING_SNAKE_CASE_     )

													if output_type == "np":
																			if image.shape[-1] == 6:
																									__UpperCamelCase       					=     np.stack([self.rgblike_to_depthmap(im[:, :, 3:]     ) for im in image]       ,		axis=0     )
																			else:
																									__UpperCamelCase       					=     image[:, :, :, 3:]
																			return image[:, :, :, :3], image_depth

													if output_type == "pil":
																			return self.numpy_to_pil(SCREAMING_SNAKE_CASE_     ), self.numpy_to_depth(SCREAMING_SNAKE_CASE_     )
													else:
																			raise Exception(F"This type {output_type} is not supported"     )






import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
				from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

				from .test_feature_extraction_speechta import floats_list


lowercase__							:							List[Any]													=     get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")






@require_torch
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     tempfile.mkdtemp()

													__UpperCamelCase       					=     SpeechTaTokenizer(SCREAMING_SNAKE_CASE_     )
													tokenizer.save_pretrained(self.tmpdirname     )

													__UpperCamelCase       					=     {
													    '''feature_size''': 1,
													    '''padding_value''': 0.0,
													    '''sampling_rate''': 16000,
													    '''do_normalize''': False,
													    '''num_mel_bins''': 80,
													    '''hop_length''': 16,
													    '''win_length''': 64,
													    '''win_function''': '''hann_window''',
													    '''fmin''': 80,
													    '''fmax''': 7600,
													    '''mel_floor''': 1E-10,
													    '''reduction_factor''': 2,
													    '''return_attention_mask''': True,
													}

													__UpperCamelCase       					=     os.path.join(self.tmpdirname       ,		SCREAMING_SNAKE_CASE_     )
													with open(self.feature_extraction_file       ,		'''w'''       ,		encoding='''utf-8'''     ) as fp:
																			fp.write(json.dumps(SCREAMING_SNAKE_CASE_     ) + '''\n'''     )



							def       A__						(					self       ,		**SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													return SpeechTaTokenizer.from_pretrained(self.tmpdirname       ,		**SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		**SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname       ,		**SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				str:





													'''simple docstring'''






													shutil.rmtree(self.tmpdirname     )



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_tokenizer()
													__UpperCamelCase       					=     self.get_feature_extractor()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													processor.save_pretrained(self.tmpdirname     )
													__UpperCamelCase       					=     SpeechTaProcessor.from_pretrained(self.tmpdirname     )

													self.assertEqual(processor.tokenizer.get_vocab()       ,		tokenizer.get_vocab()     )
													self.assertIsInstance(processor.tokenizer       ,		SCREAMING_SNAKE_CASE_     )

													self.assertEqual(processor.feature_extractor.to_json_string()       ,		feature_extractor.to_json_string()     )
													self.assertIsInstance(processor.feature_extractor       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=self.get_tokenizer()       ,		feature_extractor=self.get_feature_extractor()     )
													processor.save_pretrained(self.tmpdirname     )

													__UpperCamelCase       					=     self.get_tokenizer(bos_token='''(BOS)'''       ,		eos_token='''(EOS)'''     )
													__UpperCamelCase       					=     self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE_       ,		padding_value=1.0     )

													__UpperCamelCase       					=     SpeechTaProcessor.from_pretrained(
													    self.tmpdirname       ,		bos_token='''(BOS)'''       ,		eos_token='''(EOS)'''       ,		do_normalize=SCREAMING_SNAKE_CASE_       ,		padding_value=1.0     )

													self.assertEqual(processor.tokenizer.get_vocab()       ,		tokenizer_add_kwargs.get_vocab()     )
													self.assertIsInstance(processor.tokenizer       ,		SCREAMING_SNAKE_CASE_     )

													self.assertEqual(processor.feature_extractor.to_json_string()       ,		feature_extractor_add_kwargs.to_json_string()     )
													self.assertIsInstance(processor.feature_extractor       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     floats_list((3, 1000)     )

													__UpperCamelCase       					=     feature_extractor(audio=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )
													__UpperCamelCase       					=     processor(audio=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )

													for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()       ,		input_processor[key].sum()       ,		delta=1E-2     )



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     floats_list((3, 1000)     )

													__UpperCamelCase       					=     feature_extractor(audio_target=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )
													__UpperCamelCase       					=     processor(audio_target=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )

													for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()       ,		input_processor[key].sum()       ,		delta=1E-2     )



							def       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     '''This is a test string'''

													__UpperCamelCase       					=     processor(text=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer(SCREAMING_SNAKE_CASE_     )

													for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]       ,		encoded_processor[key]     )



							def       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     '''This is a test string'''

													__UpperCamelCase       					=     processor(text_target=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer(SCREAMING_SNAKE_CASE_     )

													for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]       ,		encoded_processor[key]     )



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													__UpperCamelCase       					=     processor.batch_decode(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     )

													self.assertListEqual(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													self.assertListEqual(
													    processor.model_input_names       ,		feature_extractor.model_input_names       ,		msg='''`processor` and `feature_extractor` model input names do not match'''       ,		)




