import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class        lowerCamelCase__				(   UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=RobertaTokenizer
			UpperCamelCase__    							=RobertaTokenizerFast
			UpperCamelCase__    							=True
			UpperCamelCase__    							={"cls_token": "<s>"}





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										_lowerCAmelCase							=							[
										    'l',
										    'o',
										    'w',
										    'e',
										    'r',
										    's',
										    't',
										    'i',
										    'd',
										    'n',
										    '\u0120',
										    '\u0120l',
										    '\u0120n',
										    '\u0120lo',
										    '\u0120low',
										    'er',
										    '\u0120lowest',
										    '\u0120newer',
										    '\u0120wider',
										    '<unk>',
										]
										_lowerCAmelCase							=							dict(zip(lowercase__  ,  range(len(lowercase__						)						)						)						)
										_lowerCAmelCase							=							['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
										_lowerCAmelCase							=							{'unk_token': '<unk>'}

										_lowerCAmelCase							=							os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES['vocab_file']						)
										_lowerCAmelCase							=							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(lowercase__						) + '\n'						)
										with open(self.merges_file  ,  'w'  ,  encoding='utf-8'						) as fp:
																	fp.write('\n'.join(lowercase__						)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple  ,  **lowercase__	:	Any						):
										kwargs.update(self.special_tokens_map						)
										return self.tokenizer_class.from_pretrained(self.tmpdirname  ,  **lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]  ,  **lowercase__	:	int						):
										kwargs.update(self.special_tokens_map						)
										return RobertaTokenizerFast.from_pretrained(self.tmpdirname  ,  **lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]  ,  lowercase__	:	Optional[Any]						):
										_lowerCAmelCase							=							'lower newer'
										_lowerCAmelCase							=							'lower newer'
										return input_text, output_text





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase							=							self.tokenizer_class(self.vocab_file  ,  self.merges_file  ,  **self.special_tokens_map						)
										_lowerCAmelCase							=							'lower newer'
										_lowerCAmelCase							=							['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
										_lowerCAmelCase							=							tokenizer.tokenize(lowercase__						)  # , add_prefix_space=True)
										self.assertListEqual(lowercase__  ,  lowercase__						)

										_lowerCAmelCase							=							tokens + [tokenizer.unk_token]
										_lowerCAmelCase							=							[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__						)  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							self.get_tokenizer()

										self.assertListEqual(tokenizer.encode('Hello world!'  ,  add_special_tokens=lowercase__						)  ,  [0, 3_14_14, 2_32, 3_28, 2]						)
										self.assertListEqual(
										    tokenizer.encode('Hello world! cécé herlolip 418'  ,  add_special_tokens=lowercase__						)  ,  [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]  ,  )





			@slow
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										_lowerCAmelCase							=							self.tokenizer_class.from_pretrained('roberta-base'						)

										_lowerCAmelCase							=							tokenizer.encode('sequence builders'  ,  add_special_tokens=lowercase__						)
										_lowerCAmelCase							=							tokenizer.encode('multi-sequence build'  ,  add_special_tokens=lowercase__						)

										_lowerCAmelCase							=							tokenizer.encode(
										    'sequence builders'  ,  add_special_tokens=lowercase__  ,  add_prefix_space=lowercase__						)
										_lowerCAmelCase							=							tokenizer.encode(
										    'sequence builders'  ,  'multi-sequence build'  ,  add_special_tokens=lowercase__  ,  add_prefix_space=lowercase__						)

										_lowerCAmelCase							=							tokenizer.build_inputs_with_special_tokens(lowercase__						)
										_lowerCAmelCase							=							tokenizer.build_inputs_with_special_tokens(lowercase__  ,  lowercase__						)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							self.get_tokenizer()

										_lowerCAmelCase							=							'Encode this sequence.'
										_lowerCAmelCase							=							tokenizer.byte_encoder[' '.encode('utf-8'						)[0]]

										# Testing encoder arguments
										_lowerCAmelCase							=							tokenizer.encode(lowercase__  ,  add_special_tokens=lowercase__  ,  add_prefix_space=lowercase__						)
										_lowerCAmelCase							=							tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
										self.assertNotEqual(lowercase__  ,  lowercase__						)

										_lowerCAmelCase							=							tokenizer.encode(lowercase__  ,  add_special_tokens=lowercase__  ,  add_prefix_space=lowercase__						)
										_lowerCAmelCase							=							tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
										self.assertEqual(lowercase__  ,  lowercase__						)

										tokenizer.add_special_tokens({'bos_token': '<s>'}						)
										_lowerCAmelCase							=							tokenizer.encode(lowercase__  ,  add_special_tokens=lowercase__						)
										_lowerCAmelCase							=							tokenizer.convert_ids_to_tokens(encoded[1]						)[0]
										self.assertNotEqual(lowercase__  ,  lowercase__						)

										# Testing spaces after special tokens
										_lowerCAmelCase							=							'<mask>'
										tokenizer.add_special_tokens(
										    {'mask_token': AddedToken(lowercase__  ,  lstrip=lowercase__  ,  rstrip=lowercase__						)}						)  # mask token has a left space
										_lowerCAmelCase							=							tokenizer.convert_tokens_to_ids(lowercase__						)

										_lowerCAmelCase							=							'Encode <mask> sequence'
										_lowerCAmelCase							=							'Encode <mask>sequence'

										_lowerCAmelCase							=							tokenizer.encode(lowercase__						)
										_lowerCAmelCase							=							encoded.index(lowercase__						)
										_lowerCAmelCase							=							tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
										self.assertEqual(lowercase__  ,  lowercase__						)

										_lowerCAmelCase							=							tokenizer.encode(lowercase__						)
										_lowerCAmelCase							=							encoded.index(lowercase__						)
										_lowerCAmelCase							=							tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
										self.assertNotEqual(lowercase__  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										pass





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'						):
																								_lowerCAmelCase							=							self.rust_tokenizer_class.from_pretrained(lowercase__  ,  **lowercase__						)
																								_lowerCAmelCase							=							self.tokenizer_class.from_pretrained(lowercase__  ,  **lowercase__						)
																								_lowerCAmelCase							=							'A, <mask> AllenNLP sentence.'
																								_lowerCAmelCase							=							tokenizer_r.encode_plus(lowercase__  ,  add_special_tokens=lowercase__  ,  return_token_type_ids=lowercase__						)
																								_lowerCAmelCase							=							tokenizer_p.encode_plus(lowercase__  ,  add_special_tokens=lowercase__  ,  return_token_type_ids=lowercase__						)

																								# token_type_ids should put 0 everywhere
																								self.assertEqual(sum(tokens_r['token_type_ids']						)  ,  sum(tokens_p['token_type_ids']						)						)

																								# attention_mask should put 1 everywhere, so sum over length should be 1
																								self.assertEqual(
																								    sum(tokens_r['attention_mask']						) / len(tokens_r['attention_mask']						)  ,  sum(tokens_p['attention_mask']						) / len(tokens_p['attention_mask']						)  ,  )

																								_lowerCAmelCase							=							tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']						)
																								_lowerCAmelCase							=							tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']						)

																								# Rust correctly handles the space before the mask while python doesnt
																								self.assertSequenceEqual(tokens_p['input_ids']  ,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]						)
																								self.assertSequenceEqual(tokens_r['input_ids']  ,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]						)

																								self.assertSequenceEqual(
																								    lowercase__  ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']						)
																								self.assertSequenceEqual(
																								    lowercase__  ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										for trim_offsets, add_prefix_space in itertools.product([True, False]  ,  repeat=2						):
																	_lowerCAmelCase							=							self.rust_tokenizer_class.from_pretrained(
																	    self.tmpdirname  ,  use_fast=lowercase__  ,  add_prefix_space=lowercase__  ,  trim_offsets=lowercase__						)

																	_lowerCAmelCase							=							json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()						)
																	_lowerCAmelCase							=							json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()						)

																	self.assertEqual(pre_tokenizer_state['add_prefix_space']  ,  lowercase__						)

																	self.assertEqual(post_processor_state['add_prefix_space']  ,  lowercase__						)
																	self.assertEqual(post_processor_state['trim_offsets']  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
										# `trim_offsets`
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'						):
																								_lowerCAmelCase							=							'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
																								_lowerCAmelCase							=							f'{text_of_1_token} {text_of_1_token}'

																								_lowerCAmelCase							=							self.rust_tokenizer_class.from_pretrained(
																								    lowercase__  ,  use_fast=lowercase__  ,  add_prefix_space=lowercase__  ,  trim_offsets=lowercase__						)
																								_lowerCAmelCase							=							tokenizer_r(lowercase__  ,  return_offsets_mapping=lowercase__  ,  add_special_tokens=lowercase__						)
																								self.assertEqual(encoding.offset_mapping[0]  ,  (0, len(lowercase__						))						)
																								self.assertEqual(
																								    encoding.offset_mapping[1]  ,  (len(lowercase__						) + 1, len(lowercase__						) + 1 + len(lowercase__						))  ,  )

																								_lowerCAmelCase							=							self.rust_tokenizer_class.from_pretrained(
																								    lowercase__  ,  use_fast=lowercase__  ,  add_prefix_space=lowercase__  ,  trim_offsets=lowercase__						)
																								_lowerCAmelCase							=							tokenizer_r(lowercase__  ,  return_offsets_mapping=lowercase__  ,  add_special_tokens=lowercase__						)
																								self.assertEqual(encoding.offset_mapping[0]  ,  (0, len(lowercase__						))						)
																								self.assertEqual(
																								    encoding.offset_mapping[1]  ,  (len(lowercase__						) + 1, len(lowercase__						) + 1 + len(lowercase__						))  ,  )

																								_lowerCAmelCase							=							self.rust_tokenizer_class.from_pretrained(
																								    lowercase__  ,  use_fast=lowercase__  ,  add_prefix_space=lowercase__  ,  trim_offsets=lowercase__						)
																								_lowerCAmelCase							=							tokenizer_r(lowercase__  ,  return_offsets_mapping=lowercase__  ,  add_special_tokens=lowercase__						)
																								self.assertEqual(encoding.offset_mapping[0]  ,  (0, len(lowercase__						))						)
																								self.assertEqual(
																								    encoding.offset_mapping[1]  ,  (len(lowercase__						), len(lowercase__						) + 1 + len(lowercase__						))  ,  )

																								_lowerCAmelCase							=							self.rust_tokenizer_class.from_pretrained(
																								    lowercase__  ,  use_fast=lowercase__  ,  add_prefix_space=lowercase__  ,  trim_offsets=lowercase__						)
																								_lowerCAmelCase							=							tokenizer_r(lowercase__  ,  return_offsets_mapping=lowercase__  ,  add_special_tokens=lowercase__						)
																								self.assertEqual(encoding.offset_mapping[0]  ,  (0, len(lowercase__						))						)
																								self.assertEqual(
																								    encoding.offset_mapping[1]  ,  (len(lowercase__						), len(lowercase__						) + 1 + len(lowercase__						))  ,  )

																								_lowerCAmelCase							=							f' {text}'

																								# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																								#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																								# )
																								# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																								# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																								# self.assertEqual(
																								#     encoding.offset_mapping[1],
																								#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																								# )

																								_lowerCAmelCase							=							self.rust_tokenizer_class.from_pretrained(
																								    lowercase__  ,  use_fast=lowercase__  ,  add_prefix_space=lowercase__  ,  trim_offsets=lowercase__						)
																								_lowerCAmelCase							=							tokenizer_r(lowercase__  ,  return_offsets_mapping=lowercase__  ,  add_special_tokens=lowercase__						)
																								self.assertEqual(encoding.offset_mapping[0]  ,  (1, 1 + len(lowercase__						))						)
																								self.assertEqual(
																								    encoding.offset_mapping[1]  ,  (1 + len(lowercase__						) + 1, 1 + len(lowercase__						) + 1 + len(lowercase__						))  ,  )

																								_lowerCAmelCase							=							self.rust_tokenizer_class.from_pretrained(
																								    lowercase__  ,  use_fast=lowercase__  ,  add_prefix_space=lowercase__  ,  trim_offsets=lowercase__						)
																								_lowerCAmelCase							=							tokenizer_r(lowercase__  ,  return_offsets_mapping=lowercase__  ,  add_special_tokens=lowercase__						)
																								self.assertEqual(encoding.offset_mapping[0]  ,  (0, 1 + len(lowercase__						))						)
																								self.assertEqual(
																								    encoding.offset_mapping[1]  ,  (1 + len(lowercase__						), 1 + len(lowercase__						) + 1 + len(lowercase__						))  ,  )

																								_lowerCAmelCase							=							self.rust_tokenizer_class.from_pretrained(
																								    lowercase__  ,  use_fast=lowercase__  ,  add_prefix_space=lowercase__  ,  trim_offsets=lowercase__						)
																								_lowerCAmelCase							=							tokenizer_r(lowercase__  ,  return_offsets_mapping=lowercase__  ,  add_special_tokens=lowercase__						)
																								self.assertEqual(encoding.offset_mapping[0]  ,  (0, 1 + len(lowercase__						))						)
																								self.assertEqual(
																								    encoding.offset_mapping[1]  ,  (1 + len(lowercase__						), 1 + len(lowercase__						) + 1 + len(lowercase__						))  ,  )







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


_lowercase:	List[str]       			=		logging.get_logger(__name__)  # pylint: disable=invalid-name






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

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

														_lowerCAmelCase							=							[np.array(i.resize((w, h)     , resample=PIL_INTERPOLATION['lanczos']  )  )[None, :] for i in image]
														_lowerCAmelCase							=							np.concatenate(snake_case     , axis=0  )
														_lowerCAmelCase							=							np.array(snake_case  ).astype(np.floataa  ) / 2_55.0
														_lowerCAmelCase							=							image.transpose(0     , 3     , 1     , 2  )
														_lowerCAmelCase							=							2.0 * image - 1.0
														_lowerCAmelCase							=							torch.from_numpy(snake_case  )
							elif isinstance(image[0]     , torch.Tensor  ):
														_lowerCAmelCase							=							torch.cat(snake_case     , dim=0  )
							return image






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

							if isinstance(mask[0]     , PIL.Image.Image  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							mask[0].size
														_lowerCAmelCase			,		_lowerCAmelCase							=							(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														_lowerCAmelCase							=							[np.array(m.convert('L'  ).resize((w, h)     , resample=PIL_INTERPOLATION['nearest']  )  )[None, :] for m in mask]
														_lowerCAmelCase							=							np.concatenate(snake_case     , axis=0  )
														_lowerCAmelCase							=							mask.astype(np.floataa  ) / 2_55.0
														_lowerCAmelCase							=							0
														_lowerCAmelCase							=							1
														_lowerCAmelCase							=							torch.from_numpy(snake_case  )
							elif isinstance(mask[0]     , torch.Tensor  ):
														_lowerCAmelCase							=							torch.cat(snake_case     , dim=0  )
							return mask



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=42
			UpperCamelCase__    							=42





			def __init__(       self	:	int  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Optional[Any]						):
										super().__init__()
										self.register_modules(unet=lowercase__  ,  scheduler=lowercase__						)





			@torch.no_grad()
			def __call__(       self	:	Any  ,  lowercase__	:	Union[torch.Tensor, PIL.Image.Image]  ,  lowercase__	:	Union[torch.Tensor, PIL.Image.Image]  ,  lowercase__	:	int = 2_50  ,  lowercase__	:	float = 0.0  ,  lowercase__	:	int = 10  ,  lowercase__	:	int = 10  ,  lowercase__	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,  lowercase__	:	Optional[str] = "pil"  ,  lowercase__	:	bool = True  ,  ):
										_lowerCAmelCase							=							image

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

										_lowerCAmelCase							=							original_image.shape[0]

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

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

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

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

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

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

										if not return_dict:
																	return (image,)

										return ImagePipelineOutput(images=lowercase__						)






