

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       __UpperCAmelCase  ( lowerCamelCase__		):
   UpperCamelCase       =  CONFIG_NAME



   @register_to_config
   def __init__(							self							:  List[Any],	__A							:  bool = True,	__A							:  int = 8,	__A							:  str = "lanczos",	__A							:  bool = True,	__A							:  bool = False,	):
     super().__init__()



   @staticmethod
   def  __magic_name__					(							__A							:  np.ndarray		):
     if images.ndim == 3:
       UpperCAmelCase :							Optional[int]								=				images[None, ...]
     UpperCAmelCase :							Union[str, Any]								=				(images * 2_5_5).round().astype('''uint8'''		)
     if images.shape[-1] == 1:
       # special case for grayscale (single channel) images
       UpperCAmelCase :							Any								=				[Image.fromarray(image.squeeze(),	mode='''L'''		) for image in images]
     else:
       UpperCAmelCase :							Any								=				[Image.fromarray(__A		) for image in images]

     return pil_images



   @staticmethod
   def  __magic_name__					(							__A							:  Union[List[PIL.Image.Image], PIL.Image.Image]		):
     if not isinstance(__A,	__A		):
       UpperCAmelCase :							Optional[Any]								=				[images]
     UpperCAmelCase :							Union[str, Any]								=				[np.array(__A		).astype(np.floataa		) / 2_5_5.0 for image in images]
     UpperCAmelCase :							Any								=				np.stack(__A,	axis=0		)

     return images



   @staticmethod
   def  __magic_name__					(							__A							:  np.ndarray		):
     if images.ndim == 3:
       UpperCAmelCase :							Tuple								=				images[..., None]

     UpperCAmelCase :							Union[str, Any]								=				torch.from_numpy(images.transpose(0,	3,	1,	2		)		)
     return images



   @staticmethod
   def  __magic_name__					(							__A							:  torch.FloatTensor		):
     UpperCAmelCase :							Optional[Any]								=				images.cpu().permute(0,	2,	3,	1		).float().numpy()
     return images



   @staticmethod
   def  __magic_name__					(							__A							:  int		):
     return 2.0 * images - 1.0



   @staticmethod
   def  __magic_name__					(							__A							:  Optional[int]		):
     return (images / 2 + 0.5).clamp(0,	1		)



   @staticmethod
   def  __magic_name__					(							__A							:  PIL.Image.Image		):
     UpperCAmelCase :							Optional[Any]								=				image.convert('''RGB'''		)
     return image



   def  __magic_name__					(							self							:  Dict,	__A							:  PIL.Image.Image,	__A							:  Optional[int] = None,	__A							:  Optional[int] = None,	):
     if height is None:
       UpperCAmelCase :							Optional[int]								=				image.height
     if width is None:
       UpperCAmelCase :							List[Any]								=				image.width

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



   def  __magic_name__					(							self							:  Dict,	__A							:  Union[torch.FloatTensor, PIL.Image.Image, np.ndarray],	__A							:  Optional[int] = None,	__A							:  Optional[int] = None,	):
     UpperCAmelCase :							int								=				(PIL.Image.Image, np.ndarray, torch.Tensor)
     if isinstance(__A,	__A		):
       UpperCAmelCase :							int								=				[image]
     elif not (isinstance(__A,	__A		) and all(isinstance(__A,	__A		) for i in image		)):
       raise ValueError(
           F'''Input is in incorrect format: {[type(__A		) for i in image]}. Currently, we only support {", ".join(__A		)}'''		)

     if isinstance(image[0],	PIL.Image.Image		):
       if self.config.do_convert_rgb:
         UpperCAmelCase :							List[Any]								=				[self.convert_to_rgb(__A		) for i in image]
       if self.config.do_resize:
         UpperCAmelCase :							Optional[int]								=				[self.resize(__A,	__A,	__A		) for i in image]
       UpperCAmelCase :							str								=				self.pil_to_numpy(__A		)  # to np
       UpperCAmelCase :							List[str]								=				self.numpy_to_pt(__A		)  # to pt

     elif isinstance(image[0],	np.ndarray		):
       UpperCAmelCase :							int								=				np.concatenate(__A,	axis=0		) if image[0].ndim == 4 else np.stack(__A,	axis=0		)
       UpperCAmelCase :							Optional[int]								=				self.numpy_to_pt(__A		)
       UpperCAmelCase				,		UpperCAmelCase				,		UpperCAmelCase				,		UpperCAmelCase :							int								=				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 :							Any								=				torch.cat(__A,	axis=0		) if image[0].ndim == 4 else torch.stack(__A,	axis=0		)
       UpperCAmelCase				,		UpperCAmelCase				,		UpperCAmelCase				,		UpperCAmelCase :							Optional[int]								=				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 :							str								=				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()}]''',	__A,	)
       UpperCAmelCase :							Optional[Any]								=				False

     if do_normalize:
       UpperCAmelCase :							List[Any]								=				self.normalize(__A		)

     return image





   def  __magic_name__					(							self							:  List[str],	__A							:  torch.FloatTensor,	__A							:  str = "pil",	__A							:  Optional[List[bool]] = None,	):
     if not isinstance(__A,	torch.Tensor		):
       raise ValueError(
           F'''Input for postprocessing is in incorrect format: {type(__A		)}. We only support pytorch tensor'''		)
     if output_type not in ["latent", "pt", "np", "pil"]:
       UpperCAmelCase :							Optional[int]								=				(
           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''',	__A,	standard_warn=__A		)
       UpperCAmelCase :							Dict								=				'''np'''

     if output_type == "latent":
       return image

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

     UpperCAmelCase :							str								=				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 :							int								=				self.pt_to_numpy(__A		)

     if output_type == "np":
       return image

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






class       __UpperCAmelCase  ( lowerCamelCase__		):
   UpperCamelCase       =  CONFIG_NAME



   @register_to_config
   def __init__(							self							:  List[str],	__A							:  bool = True,	__A							:  int = 8,	__A							:  str = "lanczos",	__A							:  bool = True,	):
     super().__init__()



   @staticmethod
   def  __magic_name__					(							__A							:  List[Any]		):
     if images.ndim == 3:
       UpperCAmelCase :							List[str]								=				images[None, ...]
     UpperCAmelCase :							Any								=				(images * 2_5_5).round().astype('''uint8'''		)
     if images.shape[-1] == 1:
       # special case for grayscale (single channel) images
       UpperCAmelCase :							Optional[int]								=				[Image.fromarray(image.squeeze(),	mode='''L'''		) for image in images]
     else:
       UpperCAmelCase :							Optional[int]								=				[Image.fromarray(image[:, :, :3]		) for image in images]

     return pil_images



   @staticmethod
   def  __magic_name__					(							__A							:  List[Any]		):
     return image[:, :, 1] * 2**8 + image[:, :, 2]



   def  __magic_name__					(							self							:  List[Any],	__A							:  str		):
     if images.ndim == 3:
       UpperCAmelCase :							List[Any]								=				images[None, ...]
     UpperCAmelCase :							Optional[int]								=				images[:, :, :, 3:]
     if images.shape[-1] == 6:
       UpperCAmelCase :							List[str]								=				(images_depth * 2_5_5).round().astype('''uint8'''		)
       UpperCAmelCase :							int								=				[
           Image.fromarray(self.rgblike_to_depthmap(__A		),	mode='''I;16'''		) for image_depth in images_depth
       ]
     elif images.shape[-1] == 4:
       UpperCAmelCase :							Tuple								=				(images_depth * 6_5_5_3_5.0).astype(np.uintaa		)
       UpperCAmelCase :							Any								=				[Image.fromarray(__A,	mode='''I;16'''		) for image_depth in images_depth]
     else:
       raise Exception('''Not supported'''		)

     return pil_images





   def  __magic_name__					(							self							:  Optional[Any],	__A							:  torch.FloatTensor,	__A							:  str = "pil",	__A							:  Optional[List[bool]] = None,	):
     if not isinstance(__A,	torch.Tensor		):
       raise ValueError(
           F'''Input for postprocessing is in incorrect format: {type(__A		)}. We only support pytorch tensor'''		)
     if output_type not in ["latent", "pt", "np", "pil"]:
       UpperCAmelCase :							str								=				(
           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''',	__A,	standard_warn=__A		)
       UpperCAmelCase :							List[str]								=				'''np'''

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

     UpperCAmelCase :							Union[str, Any]								=				torch.stack(
         [self.denormalize(image[i]		) if do_denormalize[i] else image[i] for i in range(image.shape[0]		)]		)

     UpperCAmelCase :							Dict								=				self.pt_to_numpy(__A		)

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

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








import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list





class       __UpperCAmelCase  ( unittest.TestCase		):



   def  __magic_name__					(							self							:  Dict		):
     UpperCAmelCase :							Tuple								=				'''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''		)
     UpperCAmelCase :							List[str]								=				dict(zip(__A,	range(len(__A		)		)		)		)

     UpperCAmelCase :							int								=				{
         '''pad_token''': '''<pad>''',
         '''unk_token''': '''<unk>''',
         '''bos_token''': '''<s>''',
         '''eos_token''': '''</s>''',
     }
     UpperCAmelCase :							str								=				{
         '''feature_size''': 1,
         '''padding_value''': 0.0,
         '''sampling_rate''': 1_6_0_0_0,
         '''return_attention_mask''': False,
         '''do_normalize''': True,
     }

     UpperCAmelCase :							List[Any]								=				tempfile.mkdtemp()
     UpperCAmelCase :							str								=				os.path.join(self.tmpdirname,	VOCAB_FILES_NAMES['''vocab_file''']		)
     UpperCAmelCase :							Tuple								=				os.path.join(self.tmpdirname,	__A		)
     with open(self.vocab_file,	'''w''',	encoding='''utf-8'''		) as fp:
       fp.write(json.dumps(__A		) + '''\n'''		)

     with open(self.feature_extraction_file,	'''w''',	encoding='''utf-8'''		) as fp:
       fp.write(json.dumps(__A		) + '''\n'''		)



   def  __magic_name__					(							self							:  List[Any],	**__A							:  List[Any]		):
     UpperCAmelCase :							str								=				self.add_kwargs_tokens_map.copy()
     kwargs.update(__A		)
     return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,	**__A		)



   def  __magic_name__					(							self							:  List[Any],	**__A							:  Union[str, Any]		):
     return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,	**__A		)



   def  __magic_name__					(							self							:  Any		):
     shutil.rmtree(self.tmpdirname		)



   def  __magic_name__					(							self							:  str		):
     UpperCAmelCase :							Any								=				self.get_tokenizer()
     UpperCAmelCase :							Optional[int]								=				self.get_feature_extractor()

     UpperCAmelCase :							Tuple								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     processor.save_pretrained(self.tmpdirname		)
     UpperCAmelCase :							Union[str, Any]								=				WavaVecaProcessor.from_pretrained(self.tmpdirname		)

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

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



   def  __magic_name__					(							self							:  Optional[int]		):
     UpperCAmelCase :							int								=				WavaVecaProcessor(tokenizer=self.get_tokenizer(),	feature_extractor=self.get_feature_extractor()		)
     processor.save_pretrained(self.tmpdirname		)

     UpperCAmelCase :							Dict								=				self.get_tokenizer(bos_token='''(BOS)''',	eos_token='''(EOS)'''		)
     UpperCAmelCase :							int								=				self.get_feature_extractor(do_normalize=__A,	padding_value=1.0		)

     UpperCAmelCase :							int								=				WavaVecaProcessor.from_pretrained(
         self.tmpdirname,	bos_token='''(BOS)''',	eos_token='''(EOS)''',	do_normalize=__A,	padding_value=1.0		)

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

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



   def  __magic_name__					(							self							:  Union[str, Any]		):
     UpperCAmelCase :							Optional[Any]								=				self.get_feature_extractor()
     UpperCAmelCase :							Optional[int]								=				self.get_tokenizer()

     UpperCAmelCase :							Any								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     UpperCAmelCase :							Tuple								=				floats_list((3, 1_0_0_0)		)

     UpperCAmelCase :							Any								=				feature_extractor(__A,	return_tensors='''np'''		)
     UpperCAmelCase :							int								=				processor(__A,	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  __magic_name__					(							self							:  Union[str, Any]		):
     UpperCAmelCase :							Optional[Any]								=				self.get_feature_extractor()
     UpperCAmelCase :							Union[str, Any]								=				self.get_tokenizer()

     UpperCAmelCase :							List[Any]								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     UpperCAmelCase :							List[str]								=				'''This is a test string'''

     UpperCAmelCase :							Tuple								=				processor(text=__A		)

     UpperCAmelCase :							Tuple								=				tokenizer(__A		)

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



   def  __magic_name__					(							self							:  Optional[int]		):
     UpperCAmelCase :							Tuple								=				self.get_feature_extractor()
     UpperCAmelCase :							int								=				self.get_tokenizer()

     UpperCAmelCase :							List[str]								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     UpperCAmelCase :							Any								=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCAmelCase :							str								=				processor.batch_decode(__A		)
     UpperCAmelCase :							List[str]								=				tokenizer.batch_decode(__A		)

     self.assertListEqual(__A,	__A		)





   def  __magic_name__					(							self							:  int		):
     UpperCAmelCase :							Optional[int]								=				self.get_feature_extractor()
     UpperCAmelCase :							List[str]								=				self.get_tokenizer()

     UpperCAmelCase :							Any								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

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





