


"""simple docstring"""




import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
						lowerCAmelCase__							=				'pt'
elif is_tf_available():
						lowerCAmelCase__							=				'tf'
else:
						lowerCAmelCase__							=				'jax'





class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      PerceiverTokenizer
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									super().setUp()
									SCREAMING_SNAKE_CASE_						 = PerceiverTokenizer()
									tokenizer.save_pretrained(self.tmpdirname    )

							@cached_property
							def _lowercase					(self    ):

									"""simple docstring"""







									return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''    )

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return self.tokenizer_class.from_pretrained(self.tmpdirname       ,       **A__    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=20       ,       SCREAMING_SNAKE_CASE_=5    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = []
									for i in range(len(A__    )    ):
											try:
													SCREAMING_SNAKE_CASE_						 = tokenizer.decode([i]       ,       clean_up_tokenization_spaces=A__    )
											except UnicodeDecodeError:
													pass
											toks.append((i, tok)    )

									SCREAMING_SNAKE_CASE_						 = list(filter(lambda SCREAMING_SNAKE_CASE_    : re.match(R'''^[ a-zA-Z]+$'''       ,       t[1]    )       ,       A__    )    )
									SCREAMING_SNAKE_CASE_						 = list(filter(lambda SCREAMING_SNAKE_CASE_    : [t[0]] == tokenizer.encode(t[1]       ,       add_special_tokens=A__    )       ,       A__    )    )
									if max_length is not None and len(A__    ) > max_length:
											SCREAMING_SNAKE_CASE_						 = toks[:max_length]
									if min_length is not None and len(A__    ) < min_length and len(A__    ) > 0:
											while len(A__    ) < min_length:
													SCREAMING_SNAKE_CASE_						 = toks + toks
        # toks_str = [t[1] for t in toks]
									SCREAMING_SNAKE_CASE_						 = [t[0] for t in toks]

									# Ensure consistency
									SCREAMING_SNAKE_CASE_						 = tokenizer.decode(A__       ,       clean_up_tokenization_spaces=A__    )
									if " " not in output_txt and len(A__    ) > 1:
											SCREAMING_SNAKE_CASE_						 = (
											    tokenizer.decode([toks_ids[0]]       ,       clean_up_tokenization_spaces=A__    )
											    + """ """
											    + tokenizer.decode(toks_ids[1:]       ,       clean_up_tokenization_spaces=A__    )
											)
									if with_prefix_space:
											SCREAMING_SNAKE_CASE_						 = """ """ + output_txt
									SCREAMING_SNAKE_CASE_						 = tokenizer.encode(A__       ,       add_special_tokens=A__    )
									return output_txt, output_ids

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.perceiver_tokenizer
									SCREAMING_SNAKE_CASE_						 = """Unicode €."""
									SCREAMING_SNAKE_CASE_						 = tokenizer(A__    )
									SCREAMING_SNAKE_CASE_						 = [4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
									self.assertEqual(encoded['''input_ids''']       ,       A__    )

									# decoding
									SCREAMING_SNAKE_CASE_						 = tokenizer.decode(A__    )
									self.assertEqual(A__       ,       '''[CLS]Unicode €.[SEP]'''    )

									SCREAMING_SNAKE_CASE_						 = tokenizer('''e è é ê ë'''    )
									SCREAMING_SNAKE_CASE_						 = [4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
									self.assertEqual(encoded['''input_ids''']       ,       A__    )
									# decoding
									SCREAMING_SNAKE_CASE_						 = tokenizer.decode(A__    )
									self.assertEqual(A__       ,       '''[CLS]e è é ê ë[SEP]'''    )

									# encode/decode, but with `encode` instead of `__call__`
									self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''    )    )       ,       '''[CLS]e è é ê ë[SEP]'''    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.perceiver_tokenizer
									SCREAMING_SNAKE_CASE_						 = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									# fmt: off
									SCREAMING_SNAKE_CASE_						 = [4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
									# fmt: on
									SCREAMING_SNAKE_CASE_						 = tokenizer(A__       ,       padding=A__       ,       return_tensors=A__    )
									self.assertIsInstance(A__       ,       A__    )

									if FRAMEWORK != "jax":
											SCREAMING_SNAKE_CASE_						 = list(batch.input_ids.numpy()[0]    )
									else:
											SCREAMING_SNAKE_CASE_						 = list(batch.input_ids.tolist()[0]    )

									self.assertListEqual(A__       ,       A__    )

									self.assertEqual((2, 38)       ,       batch.input_ids.shape    )
									self.assertEqual((2, 38)       ,       batch.attention_mask.shape    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.perceiver_tokenizer
									SCREAMING_SNAKE_CASE_						 = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									SCREAMING_SNAKE_CASE_						 = tokenizer(A__       ,       padding=A__       ,       return_tensors=A__    )
									# check if input_ids are returned and no decoder_input_ids
									self.assertIn('''input_ids'''       ,       A__    )
									self.assertIn('''attention_mask'''       ,       A__    )
									self.assertNotIn('''decoder_input_ids'''       ,       A__    )
									self.assertNotIn('''decoder_attention_mask'''       ,       A__    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.perceiver_tokenizer
									SCREAMING_SNAKE_CASE_						 = [
									    """Summary of the text.""",
									    """Another summary.""",
									]
									SCREAMING_SNAKE_CASE_						 = tokenizer(
									    text_target=A__       ,       max_length=32       ,       padding='''max_length'''       ,       truncation=A__       ,       return_tensors=A__    )
									self.assertEqual(32       ,       targets['''input_ids'''].shape[1]    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_tokenizers()
									for tokenizer in tokenizers:
											with self.subTest(f'{tokenizer.__class__.__name__}'    ):
													self.assertNotEqual(tokenizer.model_max_length       ,       42    )

        # Now let's start the test
									SCREAMING_SNAKE_CASE_						 = self.get_tokenizers()
									for tokenizer in tokenizers:
											with self.subTest(f'{tokenizer.__class__.__name__}'    ):
													# Isolate this from the other tests because we save additional tokens/etc
													SCREAMING_SNAKE_CASE_						 = tempfile.mkdtemp()

													SCREAMING_SNAKE_CASE_						 = """ He is very happy, UNwant\u00E9d,running"""
													SCREAMING_SNAKE_CASE_						 = tokenizer.encode(A__       ,       add_special_tokens=A__    )
													tokenizer.save_pretrained(A__    )

													SCREAMING_SNAKE_CASE_						 = tokenizer.__class__.from_pretrained(A__    )
													SCREAMING_SNAKE_CASE_						 = after_tokenizer.encode(A__       ,       add_special_tokens=A__    )
													self.assertListEqual(A__       ,       A__    )

													shutil.rmtree(A__    )

									SCREAMING_SNAKE_CASE_						 = self.get_tokenizers(model_max_length=42    )
									for tokenizer in tokenizers:
											with self.subTest(f'{tokenizer.__class__.__name__}'    ):
													# Isolate this from the other tests because we save additional tokens/etc
													SCREAMING_SNAKE_CASE_						 = tempfile.mkdtemp()

													SCREAMING_SNAKE_CASE_						 = """ He is very happy, UNwant\u00E9d,running"""
													tokenizer.add_tokens(['''bim''', '''bambam''']    )
													SCREAMING_SNAKE_CASE_						 = tokenizer.additional_special_tokens
													additional_special_tokens.append('''new_additional_special_token'''    )
													tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}    )
													SCREAMING_SNAKE_CASE_						 = tokenizer.encode(A__       ,       add_special_tokens=A__    )
													tokenizer.save_pretrained(A__    )

													SCREAMING_SNAKE_CASE_						 = tokenizer.__class__.from_pretrained(A__    )
													SCREAMING_SNAKE_CASE_						 = after_tokenizer.encode(A__       ,       add_special_tokens=A__    )
													self.assertListEqual(A__       ,       A__    )
													self.assertIn('''new_additional_special_token'''       ,       after_tokenizer.additional_special_tokens    )
													self.assertEqual(after_tokenizer.model_max_length       ,       42    )

													SCREAMING_SNAKE_CASE_						 = tokenizer.__class__.from_pretrained(A__       ,       model_max_length=43    )
													self.assertEqual(tokenizer.model_max_length       ,       43    )

													shutil.rmtree(A__    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = []
									if self.test_slow_tokenizer:
											tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())    )

									if self.test_rust_tokenizer:
											tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())    )

									for tokenizer_class, tokenizer_utils in tokenizer_list:
											with tempfile.TemporaryDirectory() as tmp_dir:
													tokenizer_utils.save_pretrained(A__    )

													with open(os.path.join(A__       ,       '''special_tokens_map.json'''    )       ,       encoding='''utf-8'''    ) as json_file:
															SCREAMING_SNAKE_CASE_						 = json.load(A__    )

													with open(os.path.join(A__       ,       '''tokenizer_config.json'''    )       ,       encoding='''utf-8'''    ) as json_file:
															SCREAMING_SNAKE_CASE_						 = json.load(A__    )

													SCREAMING_SNAKE_CASE_						 = [f'<extra_id_{i}>' for i in range(1_25    )]

													SCREAMING_SNAKE_CASE_						 = added_tokens_extra_ids + [
													    """an_additional_special_token"""
													]
													SCREAMING_SNAKE_CASE_						 = added_tokens_extra_ids + [
													    """an_additional_special_token"""
													]

													with open(os.path.join(A__       ,       '''special_tokens_map.json'''    )       ,       '''w'''       ,       encoding='''utf-8'''    ) as outfile:
															json.dump(A__       ,       A__    )
													with open(os.path.join(A__       ,       '''tokenizer_config.json'''    )       ,       '''w'''       ,       encoding='''utf-8'''    ) as outfile:
															json.dump(A__       ,       A__    )

													# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
													# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
													# "special_tokens_map.json" files
													SCREAMING_SNAKE_CASE_						 = tokenizer_class.from_pretrained(
													    A__       ,       )
													self.assertIn(
													    '''an_additional_special_token'''       ,       tokenizer_without_change_in_init.additional_special_tokens    )
													self.assertEqual(
													    ['''an_additional_special_token''']       ,       tokenizer_without_change_in_init.convert_ids_to_tokens(
													        tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']    )    )       ,       )

													# Now we test that we can change the value of additional_special_tokens in the from_pretrained
													SCREAMING_SNAKE_CASE_						 = added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''       ,       lstrip=A__    )]
													SCREAMING_SNAKE_CASE_						 = tokenizer_class.from_pretrained(
													    A__       ,       additional_special_tokens=A__       ,       )

													self.assertIn('''a_new_additional_special_token'''       ,       tokenizer.additional_special_tokens    )
													self.assertEqual(
													    ['''a_new_additional_special_token''']       ,       tokenizer.convert_ids_to_tokens(
													        tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']    )    )       ,       )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.perceiver_tokenizer
									self.assertEqual(tokenizer.decode([1_78]    )       ,       '''�'''    )

							def _lowercase					(self    ):

									"""simple docstring"""







									pass

							def _lowercase					(self    ):

									"""simple docstring"""







									pass

							def _lowercase					(self    ):

									"""simple docstring"""







									pass

							def _lowercase					(self    ):

									"""simple docstring"""







									pass







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_tokenizers(fast=A__       ,       do_lower_case=A__    )
									for tokenizer in tokenizers:
											with self.subTest(f'{tokenizer.__class__.__name__}'    ):
													SCREAMING_SNAKE_CASE_						 = ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
													SCREAMING_SNAKE_CASE_						 = tokenizer.convert_tokens_to_string(A__    )

													self.assertIsInstance(A__       ,       A__    )

"""simple docstring"""




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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowerCAmelCase__							=				logging.get_logger(__name__)  # pylint: disable=invalid-name



def        _lowerCamelCase ( __a    ):
		warnings.warn(
		    '''The preprocess method is deprecated and will be removed in a future version. Please'''
		    ''' use VaeImageProcessor.preprocess instead''',							__a,							)
		if isinstance(__a,							torch.Tensor    ):
				return image
		elif isinstance(__a,							PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE_						 = [image]

		if isinstance(image[0],							PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = image[0].size
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				SCREAMING_SNAKE_CASE_						 = [np.array(i.resize((w, h),							resample=PIL_INTERPOLATION['''lanczos''']    )    )[None, :] for i in image]
				SCREAMING_SNAKE_CASE_						 = np.concatenate(__a,							axis=0    )
				SCREAMING_SNAKE_CASE_						 = np.array(__a    ).astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE_						 = image.transpose(0,							3,							1,							2    )
				SCREAMING_SNAKE_CASE_						 = 2.0 * image - 1.0
				SCREAMING_SNAKE_CASE_						 = torch.from_numpy(__a    )
		elif isinstance(image[0],							torch.Tensor    ):
				SCREAMING_SNAKE_CASE_						 = torch.cat(__a,							dim=0    )
		return image



def        _lowerCamelCase ( __a    ):
		if isinstance(__a,							torch.Tensor    ):
				return mask
		elif isinstance(__a,							PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE_						 = [mask]

		if isinstance(mask[0],							PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = mask[0].size
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				SCREAMING_SNAKE_CASE_						 = [np.array(m.convert('''L'''    ).resize((w, h),							resample=PIL_INTERPOLATION['''nearest''']    )    )[None, :] for m in mask]
				SCREAMING_SNAKE_CASE_						 = np.concatenate(__a,							axis=0    )
				SCREAMING_SNAKE_CASE_						 = mask.astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE_						 = 0
				SCREAMING_SNAKE_CASE_						 = 1
				SCREAMING_SNAKE_CASE_						 = torch.from_numpy(__a    )
		elif isinstance(mask[0],							torch.Tensor    ):
				SCREAMING_SNAKE_CASE_						 = torch.cat(__a,							dim=0    )
		return mask





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      42
							UpperCAmelCase__						     =      42

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									super().__init__()
									self.register_modules(unet=SCREAMING_SNAKE_CASE_       ,       scheduler=SCREAMING_SNAKE_CASE_    )







							@torch.no_grad()
							def __call__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = 2_50       ,       SCREAMING_SNAKE_CASE_ = 0.0       ,       SCREAMING_SNAKE_CASE_ = 10       ,       SCREAMING_SNAKE_CASE_ = 10       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = "pil"       ,       SCREAMING_SNAKE_CASE_ = True       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = image

									SCREAMING_SNAKE_CASE_						 = _preprocess_image(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = original_image.to(device=self.device       ,       dtype=self.unet.dtype    )
									SCREAMING_SNAKE_CASE_						 = _preprocess_mask(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = mask_image.to(device=self.device       ,       dtype=self.unet.dtype    )

									SCREAMING_SNAKE_CASE_						 = original_image.shape[0]

									# sample gaussian noise to begin the loop
									if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) and len(SCREAMING_SNAKE_CASE_    ) != batch_size:
											raise ValueError(
											    f'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_    )}, but requested an effective batch'
											    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'    )

									SCREAMING_SNAKE_CASE_						 = original_image.shape
									SCREAMING_SNAKE_CASE_						 = randn_tensor(SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_       ,       device=self.device       ,       dtype=self.unet.dtype    )

									# set step values
									self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       self.device    )
									SCREAMING_SNAKE_CASE_						 = eta

									SCREAMING_SNAKE_CASE_						 = self.scheduler.timesteps[0] + 1
									SCREAMING_SNAKE_CASE_						 = generator[0] if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) else generator
									for i, t in enumerate(self.progress_bar(self.scheduler.timesteps    )    ):
											if t < t_last:
													# predict the noise residual
													SCREAMING_SNAKE_CASE_						 = self.unet(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ).sample
													# compute previous image: x_t -> x_t-1
													SCREAMING_SNAKE_CASE_						 = self.scheduler.step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ).prev_sample

											else:
													# compute the reverse: x_t-1 -> x_t
													SCREAMING_SNAKE_CASE_						 = self.scheduler.undo_step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = t

									SCREAMING_SNAKE_CASE_						 = (image / 2 + 0.5).clamp(0       ,       1    )
									SCREAMING_SNAKE_CASE_						 = image.cpu().permute(0       ,       2       ,       3       ,       1    ).numpy()
									if output_type == "pil":
											SCREAMING_SNAKE_CASE_						 = self.numpy_to_pil(SCREAMING_SNAKE_CASE_    )

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_    )