




'''simple docstring'''







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  (     A__			):
       __a    :					List[Any]           = CONFIG_NAME
       @register_to_config
       def __init__(     self						:     List[str]       ,    lowercase						:     Union[str, Any] = True       ,    lowercase						:     List[Any] = 8       ,    lowercase						:     Tuple = "lanczos"       ,    lowercase						:     Optional[int] = True       ,    lowercase						:     Optional[int] = False       ,    ):



           '''simple docstring'''

           super().__init__()
       @staticmethod
       def    A				(     lowercase						:     Optional[int]    ):



           '''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(snake_case_    ) for image in images]

           return pil_images
       @staticmethod
       def    A				(     lowercase						:     List[Any]    ):



           '''simple docstring'''

           if not isinstance(snake_case_       ,    snake_case_    ):
               UpperCAmelCase      	=		[images]
           UpperCAmelCase      	=		[np.array(snake_case_    ).astype(np.floataa    ) / 255.0 for image in images]
           UpperCAmelCase      	=		np.stack(snake_case_       ,    axis=0    )

           return images
       @staticmethod
       def    A				(     lowercase						:     int    ):



           '''simple docstring'''

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

           UpperCAmelCase      	=		torch.from_numpy(images.transpose(0       ,    3       ,    1       ,    2    )    )
           return images
       @staticmethod
       def    A				(     lowercase						:     Tuple    ):



           '''simple docstring'''

           UpperCAmelCase      	=		images.cpu().permute(0       ,    2       ,    3       ,    1    ).float().numpy()
           return images
       @staticmethod
       def    A				(     lowercase						:     List[str]    ):



           '''simple docstring'''

           return 2.0 * images - 1.0
       @staticmethod
       def    A				(     lowercase						:     Union[str, Any]    ):



           '''simple docstring'''

           return (images / 2 + 0.5).clamp(0       ,    1    )
       @staticmethod
       def    A				(     lowercase						:     str    ):



           '''simple docstring'''

           UpperCAmelCase      	=		image.convert('''RGB'''    )
           return image
       def    A				(     self						:     Optional[Any]       ,    lowercase						:     Any       ,    lowercase						:     Tuple = None       ,    lowercase						:     Tuple = None       ,    ):



           '''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						:     int       ,    lowercase						:     Dict       ,    lowercase						:     Optional[int] = None       ,    lowercase						:     Any = None       ,    ):



           '''simple docstring'''

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

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

           elif isinstance(image[0]       ,    np.ndarray    ):
               UpperCAmelCase      	=		np.concatenate(snake_case_       ,    axis=0    ) if image[0].ndim == 4 else np.stack(snake_case_       ,    axis=0    )
               UpperCAmelCase      	=		self.numpy_to_pt(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(snake_case_       ,    axis=0    ) if image[0].ndim == 4 else torch.stack(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()}]"       ,    snake_case_       ,    )
               UpperCAmelCase      	=		False

           if do_normalize:
               UpperCAmelCase      	=		self.normalize(snake_case_    )

           return image







       def    A				(     self						:     Optional[Any]       ,    lowercase						:     Union[str, Any]       ,    lowercase						:     Optional[int] = "pil"       ,    lowercase						:     Union[str, Any] = None       ,    ):



           '''simple docstring'''

           if not isinstance(snake_case_       ,    torch.Tensor    ):
               raise ValueError(
                   f"Input for postprocessing is in incorrect format: {type(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'''       ,    snake_case_       ,    standard_warn=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(snake_case_    )

           if output_type == "np":
               return image

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






class       _a  (     A__			):
       __a    :					Optional[Any]           = CONFIG_NAME
       @register_to_config
       def __init__(     self						:     Any       ,    lowercase						:     Optional[Any] = True       ,    lowercase						:     Optional[int] = 8       ,    lowercase						:     str = "lanczos"       ,    lowercase						:     Dict = True       ,    ):



           '''simple docstring'''

           super().__init__()
       @staticmethod
       def    A				(     lowercase						:     List[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				(     lowercase						:     Union[str, Any]    ):



           '''simple docstring'''

           return image[:, :, 1] * 2**8 + image[:, :, 2]
       def    A				(     self						:     List[Any]       ,    lowercase						:     Optional[Any]    ):



           '''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(snake_case_    )       ,    mode='''I;16'''    ) for image_depth in images_depth
               ]
           elif images.shape[-1] == 4:
               UpperCAmelCase      	=		(images_depth * 65_535.0).astype(np.uintaa    )
               UpperCAmelCase      	=		[Image.fromarray(snake_case_       ,    mode='''I;16'''    ) for image_depth in images_depth]
           else:
               raise Exception('''Not supported'''    )

           return pil_images







       def    A				(     self						:     Tuple       ,    lowercase						:     str       ,    lowercase						:     List[Any] = "pil"       ,    lowercase						:     str = None       ,    ):



           '''simple docstring'''

           if not isinstance(snake_case_       ,    torch.Tensor    ):
               raise ValueError(
                   f"Input for postprocessing is in incorrect format: {type(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'''       ,    snake_case_       ,    standard_warn=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(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(snake_case_    ), self.numpy_to_depth(snake_case_    )
           else:
               raise Exception(f"This type {output_type} is not supported"    )








'''simple docstring'''







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class       _a  (     __a							,							unittest.TestCase			):
       __a    :					str           = HerbertTokenizer
       __a    :					Optional[Any]           = HerbertTokenizerFast
       __a    :					Any           = True
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           super().setUp()

           # Use a simpler test file without japanese/chinese characters
           with open(f"{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"       ,    encoding='''utf-8'''    ) as f_data:
               UpperCAmelCase      	=		f_data.read().replace('''\n\n'''       ,    '''\n'''    ).strip()

           UpperCAmelCase      	=		[
               '''<s>''',
               '''</s>''',
               '''l''',
               '''o''',
               '''w''',
               '''e''',
               '''r''',
               '''s''',
               '''t''',
               '''i''',
               '''d''',
               '''n''',
               '''w</w>''',
               '''r</w>''',
               '''t</w>''',
               '''lo''',
               '''low''',
               '''er</w>''',
               '''low</w>''',
               '''lowest</w>''',
               '''newer</w>''',
               '''wider</w>''',
               ''',</w>''',
               '''<unk>''',
           ]
           UpperCAmelCase      	=		dict(zip(lowercase       ,    range(len(lowercase    )    )    )    )
           UpperCAmelCase      	=		['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''vocab_file''']    )
           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''merges_file''']    )
           with open(self.vocab_file       ,    '''w'''    ) as fp:
               fp.write(json.dumps(lowercase    )    )
           with open(self.merges_file       ,    '''w'''    ) as fp:
               fp.write('''\n'''.join(lowercase    )    )
       def    A				(     self						:     List[Any]       ,    lowercase						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		'''lower newer'''
           UpperCAmelCase      	=		'''lower newer'''
           return input_text, output_text
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(vocab_file=self.vocab_file       ,    merges_file=self.merges_file    )

           UpperCAmelCase      	=		'''lower'''
           UpperCAmelCase      	=		['''low''', '''er</w>''']
           UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		tokens + ['''<unk>''']
           UpperCAmelCase      	=		[16, 17, 23]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase    )       ,    lowercase    )
       def    A				(     self						:     str    ):



           '''simple docstring'''

           if not self.test_rust_tokenizer:
               return

           UpperCAmelCase      	=		self.get_tokenizer()
           UpperCAmelCase      	=		self.get_rust_tokenizer()

           UpperCAmelCase      	=		'''lower,newer'''

           UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.tokenize(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		tokenizer.encode(lowercase       ,    add_special_tokens=lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.encode(lowercase       ,    add_special_tokens=lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		self.get_rust_tokenizer()
           UpperCAmelCase      	=		tokenizer.encode(lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.encode(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )







       @slow
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''    )

           UpperCAmelCase      	=		tokenizer.encode('''konstruowanie sekwencji'''       ,    add_special_tokens=lowercase    )
           UpperCAmelCase      	=		tokenizer.encode('''konstruowanie wielu sekwencji'''       ,    add_special_tokens=lowercase    )

           UpperCAmelCase      	=		tokenizer.build_inputs_with_special_tokens(lowercase    )
           UpperCAmelCase      	=		tokenizer.build_inputs_with_special_tokens(lowercase       ,    lowercase    )

           assert encoded_sentence == [0] + text + [2]
           assert encoded_pair == [0] + text + [2] + text_a + [2]




