




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 		A__				(   __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: Any				    =CONFIG_NAME






	@register_to_config
	def __init__(	self				,     lowerCamelCase = True				,     lowerCamelCase = 8				,     lowerCamelCase = "lanczos"				,     lowerCamelCase = True				,     lowerCamelCase = False				,     )      ->   Any:




						"""simple docstring"""






						super().__init__()






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   PIL.Image.Image:




						"""simple docstring"""






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

						return pil_images






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   np.ndarray:




						"""simple docstring"""






						if not isinstance(lowerCamelCase				,     lowerCamelCase						):
											__magic_name__					:							List[str]		   =							[images]
						__magic_name__					:							Optional[Any]		   =							[np.array(lowerCamelCase						).astype(np.floataa						) / 2_5_5.0 for image in images]
						__magic_name__					:							List[str]		   =							np.stack(lowerCamelCase				,     axis=0						)

						return images






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   torch.FloatTensor:




						"""simple docstring"""






						if images.ndim == 3:
											__magic_name__					:							List[str]		   =							images[..., None]

						__magic_name__					:							Any		   =							torch.from_numpy(images.transpose(0				,     3				,     1				,     2						)						)
						return images






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   np.ndarray:




						"""simple docstring"""






						__magic_name__					:							Optional[int]		   =							images.cpu().permute(0				,     2				,     3				,     1						).float().numpy()
						return images






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   Tuple:




						"""simple docstring"""






						return 2.0 * images - 1.0






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   Tuple:




						"""simple docstring"""






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






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   PIL.Image.Image:




						"""simple docstring"""






						__magic_name__					:							Dict		   =							image.convert('''RGB'''						)
						return image






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase = None				,     lowerCamelCase = None				,     )      ->   PIL.Image.Image:




						"""simple docstring"""






						if height is None:
											__magic_name__					:							Dict		   =							image.height
						if width is None:
											__magic_name__					:							str		   =							image.width

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






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase = None				,     lowerCamelCase = None				,     )      ->   torch.Tensor:




						"""simple docstring"""






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

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

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

						if do_normalize:
											__magic_name__					:							Dict		   =							self.normalize(lowerCamelCase						)

						return image






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase = "pil"				,     lowerCamelCase = None				,     )      ->   Any:




						"""simple docstring"""






						if not isinstance(lowerCamelCase				,     torch.Tensor						):
											raise ValueError(
											    F'''Input for postprocessing is in incorrect format: {type(lowerCamelCase						)}. We only support pytorch tensor'''						)
						if output_type not in ["latent", "pt", "np", "pil"]:
											__magic_name__					:							Union[str, Any]		   =							(
											    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'''				,     lowerCamelCase				,     standard_warn=lowerCamelCase						)
											__magic_name__					:							Union[str, Any]		   =							'''np'''

						if output_type == "latent":
											return image

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

						__magic_name__					:							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

						__magic_name__					:							int		   =							self.pt_to_numpy(lowerCamelCase						)

						if output_type == "np":
											return image

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





class 		A__				(   __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: Optional[int]				    =CONFIG_NAME






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




						"""simple docstring"""






						super().__init__()






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   Tuple:




						"""simple docstring"""






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

						return pil_images






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   Any:




						"""simple docstring"""






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






	def  lowercase      (	self				,     lowerCamelCase						)      ->   str:




						"""simple docstring"""






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

						return pil_images






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase = "pil"				,     lowerCamelCase = None				,     )      ->   List[Any]:




						"""simple docstring"""






						if not isinstance(lowerCamelCase				,     torch.Tensor						):
											raise ValueError(
											    F'''Input for postprocessing is in incorrect format: {type(lowerCamelCase						)}. We only support pytorch tensor'''						)
						if output_type not in ["latent", "pt", "np", "pil"]:
											__magic_name__					:							Dict		   =							(
											    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'''				,     lowerCamelCase				,     standard_warn=lowerCamelCase						)
											__magic_name__					:							Optional[int]		   =							'''np'''

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

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

						__magic_name__					:							Any		   =							self.pt_to_numpy(lowerCamelCase						)

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

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









import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin
class 				__snake_case     (_a  ,		unittest.TestCase						):
				lowerCAmelCase__			=   SpeechaTextaTokenizer
				lowerCAmelCase__			=   False



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





								'''simple docstring'''




								super().setUp()

								_lowerCAmelCase			:			Optional[int]        =					"""<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab""".split(""" """					)
								_lowerCAmelCase			:			Optional[int]        =					["""he re</w> 123""", """here a 1456"""]
								_lowerCAmelCase			:			Union[str, Any]        =					dict(zip(_UpperCAmelCase						,		range(len(_UpperCAmelCase					)					)					)					)

								_lowerCAmelCase			:			Any        =					{"""pad_token""": """<pad>""", """unk_token""": """<unk>""", """bos_token""": """<s>""", """eos_token""": """</s>"""}

								_lowerCAmelCase			:			Union[str, Any]        =					tempfile.mkdtemp()
								_lowerCAmelCase			:			Tuple        =					os.path.join(self.tmpdirname						,		VOCAB_FILES_NAMES["""vocab_file"""]					)
								_lowerCAmelCase			:			Any        =					os.path.join(self.tmpdirname						,		VOCAB_FILES_NAMES["""merges_file"""]					)

								with open(self.vocab_file						,		"""w"""						,		encoding="""utf-8"""					) as fp:
												fp.write(json.dumps(_UpperCAmelCase					) + """\n"""					)

								with open(self.merges_file						,		"""w"""					) as fp:
												fp.write("""\n""".join(_UpperCAmelCase					)					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					list(self.get_tokenizer().get_vocab().keys()					)

								self.assertEqual(vocab_keys[0]						,		"""<s>"""					)
								self.assertEqual(vocab_keys[1]						,		"""<pad>"""					)
								self.assertEqual(vocab_keys[-1]						,		"""vocab"""					)
								self.assertEqual(len(_UpperCAmelCase					)						,		14					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			int:





								'''simple docstring'''




								self.assertEqual(self.get_tokenizer().vocab_size						,		14					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						str					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

								# make sure @@ is correctly concatenated
								_lowerCAmelCase			:			int        =					[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
								_lowerCAmelCase			:			List[Any]        =					tokenizer.decode(_UpperCAmelCase					)

								self.assertTrue(output_string == """herecouple words ofthe"""					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			str        =					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

								with tempfile.TemporaryDirectory() as tmp_dirname:
												tokenizer.save_pretrained(_UpperCAmelCase					)
												os.remove(os.path.join(_UpperCAmelCase						,		"""merges.txt"""					)					)

												# load tokenizer without merges file should not throw an error
												_lowerCAmelCase			:			Any        =					SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase					)

								with tempfile.TemporaryDirectory() as tmp_dirname:
												# save tokenizer and load again
												tokenizer.save_pretrained(_UpperCAmelCase					)
												_lowerCAmelCase			:			int        =					SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase					)

								self.assertIsNotNone(_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			Optional[Any]:





								'''simple docstring'''




								if not self.test_slow_tokenizer:
												return

								_lowerCAmelCase			:			Dict        =					inspect.signature(self.tokenizer_class.__init__					)
								_lowerCAmelCase			:			Union[str, Any]        =					self.get_tokenizer()

								for parameter_name, parameter in signature.parameters.items():
												if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																self.assertIn(_UpperCAmelCase						,		tokenizer.init_kwargs					)

