


import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







class      lowerCAmelCase__       (				a     ,    unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= ProphetNetTokenizer
     __SCREAMING_SNAKE_CASE  			= False


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            super().setUp()

            _A      :       List[str]				    = [
                "[UNK]",
                "[CLS]",
                "[SEP]",
                "[PAD]",
                "[MASK]",
                "want",
                "##want",
                "##ed",
                "wa",
                "un",
                "runn",
                "##ing",
                ",",
                "low",
                "lowest",
            ]
            _A      :       Union[str, Any]				    = os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES["vocab_file"])
            with open(self.vocab_file ,						"w" ,						encoding="utf-8") as vocab_writer:
                   vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Optional[Any]:
            _A      :       Optional[Any]				    = "UNwant\u00E9d,running"
            _A      :       Optional[Any]				    = "unwanted, running"
            return input_text, output_text


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       Optional[int]				    = self.tokenizer_class(self.vocab_file)

            _A      :       Optional[int]				    = tokenizer.tokenize("UNwant\u00E9d,running")
            self.assertListEqual(__lowerCamelCase ,						["un", "##want", "##ed", ",", "runn", "##ing"])
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase) ,						[9, 6, 7, 1_2, 1_0, 1_1])


     def  _lowerCamelCase (						self)			->				Any:
            _A      :       Tuple				    = BasicTokenizer()

            self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz") ,						["ah", "\u535A", "\u63A8", "zz"])


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Tuple				    = BasicTokenizer(do_lower_case=__lowerCamelCase)

            self.assertListEqual(
                tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  ") ,						["hello", "!", "how", "are", "you", "?"])
            self.assertListEqual(tokenizer.tokenize("H\u00E9llo") ,						["hello"])


     def  _lowerCamelCase (						self)			->				int:
            _A      :       Dict				    = BasicTokenizer(do_lower_case=__lowerCamelCase ,						strip_accents=__lowerCamelCase)

            self.assertListEqual(
                tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  ") ,						["hällo", "!", "how", "are", "you", "?"])
            self.assertListEqual(tokenizer.tokenize("H\u00E9llo") ,						["h\u00E9llo"])


     def  _lowerCamelCase (						self)			->				str:
            _A      :       int				    = BasicTokenizer(do_lower_case=__lowerCamelCase ,						strip_accents=__lowerCamelCase)

            self.assertListEqual(
                tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  ") ,						["hallo", "!", "how", "are", "you", "?"])
            self.assertListEqual(tokenizer.tokenize("H\u00E9llo") ,						["hello"])


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       Tuple				    = BasicTokenizer(do_lower_case=__lowerCamelCase)

            self.assertListEqual(
                tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  ") ,						["hallo", "!", "how", "are", "you", "?"])
            self.assertListEqual(tokenizer.tokenize("H\u00E9llo") ,						["hello"])


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       List[Any]				    = BasicTokenizer(do_lower_case=__lowerCamelCase)

            self.assertListEqual(
                tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  ") ,						["HeLLo", "!", "how", "Are", "yoU", "?"])


     def  _lowerCamelCase (						self)			->				Any:
            _A      :       Optional[int]				    = BasicTokenizer(do_lower_case=__lowerCamelCase ,						strip_accents=__lowerCamelCase)

            self.assertListEqual(
                tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  ") ,						["HäLLo", "!", "how", "Are", "yoU", "?"])


     def  _lowerCamelCase (						self)			->				int:
            _A      :       List[str]				    = BasicTokenizer(do_lower_case=__lowerCamelCase ,						strip_accents=__lowerCamelCase)

            self.assertListEqual(
                tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  ") ,						["HaLLo", "!", "how", "Are", "yoU", "?"])


     def  _lowerCamelCase (						self)			->				Any:
            _A      :       List[Any]				    = BasicTokenizer(do_lower_case=__lowerCamelCase ,						never_split=["[UNK]"])

            self.assertListEqual(
                tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]") ,						["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"])


     def  _lowerCamelCase (						self)			->				str:
            _A      :       int				    = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

            _A      :       List[Any]				    = {}
            for i, token in enumerate(__lowerCamelCase):
                   _A      :       Tuple				    = i
            _A      :       Dict				    = WordpieceTokenizer(vocab=__lowerCamelCase ,						unk_token="[UNK]")

            self.assertListEqual(tokenizer.tokenize("") ,						[])

            self.assertListEqual(tokenizer.tokenize("unwanted running") ,						["un", "##want", "##ed", "runn", "##ing"])

            self.assertListEqual(tokenizer.tokenize("unwantedX running") ,						["[UNK]", "runn", "##ing"])


     @require_torch
     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       Tuple				    = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased")

            _A      :       int				    = ["A long paragraph for summarization.", "Another paragraph for summarization."]
            _A      :       int				    = [1_0_3_7, 2_1_4_6, 2_0_4_2_3, 2_0_0_5, 7_6_8_0, 7_8_4_9, 3_9_8_9, 1_0_1_2, 1_0_2]
            _A      :       Optional[Any]				    = tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors="pt")
            self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase)
            _A      :       Dict				    = list(batch.input_ids.numpy()[0])
            self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase)

            self.assertEqual((2, 9) ,						batch.input_ids.shape)
            self.assertEqual((2, 9) ,						batch.attention_mask.shape)


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            self.assertTrue(_is_whitespace(" "))
            self.assertTrue(_is_whitespace("\t"))
            self.assertTrue(_is_whitespace("\r"))
            self.assertTrue(_is_whitespace("\n"))
            self.assertTrue(_is_whitespace("\u00A0"))

            self.assertFalse(_is_whitespace("A"))
            self.assertFalse(_is_whitespace("-"))


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            self.assertTrue(_is_control("\u0005"))

            self.assertFalse(_is_control("A"))
            self.assertFalse(_is_control(" "))
            self.assertFalse(_is_control("\t"))
            self.assertFalse(_is_control("\r"))


     def  _lowerCamelCase (						self)			->				Dict:
            self.assertTrue(_is_punctuation("-"))
            self.assertTrue(_is_punctuation("$"))
            self.assertTrue(_is_punctuation("`"))
            self.assertTrue(_is_punctuation("."))

            self.assertFalse(_is_punctuation("A"))
            self.assertFalse(_is_punctuation(" "))


     @slow
     def  _lowerCamelCase (						self)			->				Tuple:
            _A      :       List[Any]				    = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased")

            _A      :       str				    = tokenizer.encode("sequence builders" ,						add_special_tokens=__lowerCamelCase)
            _A      :       str				    = tokenizer.encode("multi-sequence build" ,						add_special_tokens=__lowerCamelCase)

            _A      :       List[str]				    = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase)
            _A      :       Dict				    = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase ,						__lowerCamelCase)

            assert encoded_sentence == text + [1_0_2]
            assert encoded_pair == text + [1_0_2] + text_a + [1_0_2]




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]						):
       _A    ,			_A      :       Any				    = image.size
       _A    ,			_A      :       str				    = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
       _A      :       List[str]				    = image.resize((w, h)				,							resample=PIL_INTERPOLATION["lanczos"]						)
       _A      :       Any				    = np.array(UpperCamelCase__						).astype(np.floataa						) / 2_55.0
       _A      :       Optional[Any]				    = image[None].transpose(0				,							3				,							1				,							2						)
       _A      :       Union[str, Any]				    = torch.from_numpy(UpperCamelCase__						)
       return 2.0 * image - 1.0







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						)			->				Optional[int]:
            super().__init__()
            self.register_modules(vqvae=__lowerCamelCase ,						unet=__lowerCamelCase ,						scheduler=__lowerCamelCase)


     @torch.no_grad()
     def __call__(						self ,						__lowerCamelCase = None ,						__lowerCamelCase = 1 ,						__lowerCamelCase = 1_0_0 ,						__lowerCamelCase = 0.0 ,						__lowerCamelCase = None ,						__lowerCamelCase = "pil" ,						__lowerCamelCase = True ,						)			->				Union[Tuple, ImagePipelineOutput]:
            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Tuple				    = 1
            elif isinstance(__lowerCamelCase ,						torch.Tensor):
                   _A      :       Union[str, Any]				    = image.shape[0]
            else:
                   raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase)}")

            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Union[str, Any]				    = preprocess(__lowerCamelCase)

            _A    ,			_A      :       Union[str, Any]				    = image.shape[-2:]

            # in_channels should be 6: 3 for latents, 3 for low resolution image
            _A      :       Optional[Any]				    = (batch_size, self.unet.config.in_channels // 2, height, width)
            _A      :       str				    = next(self.unet.parameters()).dtype

            _A      :       Union[str, Any]				    = randn_tensor(__lowerCamelCase ,						generator=__lowerCamelCase ,						device=self.device ,						dtype=__lowerCamelCase)

            _A      :       List[Any]				    = image.to(device=self.device ,						dtype=__lowerCamelCase)

            # set timesteps and move to the correct device
            self.scheduler.set_timesteps(__lowerCamelCase ,						device=self.device)
            _A      :       Any				    = self.scheduler.timesteps

            # scale the initial noise by the standard deviation required by the scheduler
            _A      :       List[str]				    = latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            _A      :       str				    = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
            _A      :       Optional[int]				    = {}
            if accepts_eta:
                   _A      :       List[Any]				    = eta

            for t in self.progress_bar(__lowerCamelCase):
                   # concat latents and low resolution image in the channel dimension.
                   _A      :       List[Any]				    = torch.cat([latents, image] ,						dim=1)
                   _A      :       str				    = self.scheduler.scale_model_input(__lowerCamelCase ,						__lowerCamelCase)
                   # predict the noise residual
                   _A      :       Any				    = self.unet(__lowerCamelCase ,						__lowerCamelCase).sample
                   # compute the previous noisy sample x_t -> x_t-1
                   _A      :       Optional[int]				    = self.scheduler.step(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase).prev_sample

            # decode the image latents with the VQVAE
            _A      :       Union[str, Any]				    = self.vqvae.decode(__lowerCamelCase).sample
            _A      :       Dict				    = torch.clamp(__lowerCamelCase ,						-1.0 ,						1.0)
            _A      :       Tuple				    = image / 2 + 0.5
            _A      :       int				    = image.cpu().permute(0 ,						2 ,						3 ,						1).numpy()

            if output_type == "pil":
                   _A      :       Optional[int]				    = self.numpy_to_pil(__lowerCamelCase)

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=__lowerCamelCase)
