
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE   :		Tuple					 =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE   :		List[str]					 =       {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

SCREAMING_SNAKE_CASE   :		Any					 =       {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
}

SCREAMING_SNAKE_CASE   :		str					 =       {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}



@lru_cache()
def        lowerCamelCase_  (   ):
				A_       	=  (
				    list(range(ord('''!'''     ) ,						ord('''~'''     ) + 1     )     ) + list(range(ord('''¡'''     ) ,						ord('''¬'''     ) + 1     )     ) + list(range(ord('''®'''     ) ,						ord('''ÿ'''     ) + 1     )     )
				)
				A_       	=  bs[:]
				A_       	=  0
				for b in range(2**8     ):
								if b not in bs:
												bs.append(__UpperCamelCase     )
												cs.append(2**8 + n     )
												n += 1
				A_       	=  [chr(__UpperCamelCase     ) for n in cs]
				return dict(zip(__UpperCamelCase ,						__UpperCamelCase     )     )



def        lowerCamelCase_  (   __UpperCamelCase     ):
				A_       	=  set()
				A_       	=  word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)     )
								A_       	=  char
				return pairs






class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	VOCAB_FILES_NAMES
					__magic_name__						:							List[Any]     	=	PRETRAINED_VOCAB_FILES_MAP
					__magic_name__						:							Tuple     	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__magic_name__						:							Union[str, Any]     	=	['''input_ids''', '''attention_mask''']


					def __init__(	self       ,       a__       ,       a__       ,       a__="replace"       ,       a__="<s>"       ,       a__="</s>"       ,       a__="</s>"       ,       a__="<s>"       ,       a__="<unk>"       ,       a__="<pad>"       ,       a__="<mask>"       ,       a__=False       ,       **a__       ,       )		->							str:





									'''simple docstring'''
									A_       	=  AddedToken(a__       ,       lstrip=a__       ,       rstrip=a__						) if isinstance(a__       ,       a__						) else bos_token
									A_       	=  AddedToken(a__       ,       lstrip=a__       ,       rstrip=a__						) if isinstance(a__       ,       a__						) else eos_token
									A_       	=  AddedToken(a__       ,       lstrip=a__       ,       rstrip=a__						) if isinstance(a__       ,       a__						) else sep_token
									A_       	=  AddedToken(a__       ,       lstrip=a__       ,       rstrip=a__						) if isinstance(a__       ,       a__						) else cls_token
									A_       	=  AddedToken(a__       ,       lstrip=a__       ,       rstrip=a__						) if isinstance(a__       ,       a__						) else unk_token
									A_       	=  AddedToken(a__       ,       lstrip=a__       ,       rstrip=a__						) if isinstance(a__       ,       a__						) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									A_       	=  AddedToken(a__       ,       lstrip=a__       ,       rstrip=a__						) if isinstance(a__       ,       a__						) else mask_token

									super().__init__(
									    errors=a__       ,       bos_token=a__       ,       eos_token=a__       ,       unk_token=a__       ,       sep_token=a__       ,       cls_token=a__       ,       pad_token=a__       ,       mask_token=a__       ,       add_prefix_space=a__       ,       **a__       ,       )

									with open(a__       ,       encoding='''utf-8'''						) as vocab_handle:
													A_       	=  json.load(a__						)
									A_       	=  {v: k for k, v in self.encoder.items()}
									A_       	=  errors  # how to handle errors in decoding
									A_       	=  bytes_to_unicode()
									A_       	=  {v: k for k, v in self.byte_encoder.items()}
									with open(a__       ,       encoding='''utf-8'''						) as merges_handle:
													A_       	=  merges_handle.read().split('''\n'''						)[1:-1]
									A_       	=  [tuple(merge.split()						) for merge in bpe_merges]
									A_       	=  dict(zip(a__       ,       range(len(a__						)						)						)						)
									A_       	=  {}
									A_       	=  add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									A_       	=  re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''						)


					@property
					def        lowerCAmelCase_    (	self						)		->							str:





									'''simple docstring'''
									return len(self.encoder						)


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





									'''simple docstring'''
									return dict(self.encoder       ,       **self.added_tokens_encoder						)


					def        lowerCAmelCase_    (	self       ,       a__						)		->							int:





									'''simple docstring'''
									if token in self.cache:
													return self.cache[token]
									A_       	=  tuple(a__						)
									A_       	=  get_pairs(a__						)

									if not pairs:
													return token

									while True:
													A_       	=  min(a__       ,       key=lambda a__						: self.bpe_ranks.get(a__       ,       float('''inf'''						)						)						)
													if bigram not in self.bpe_ranks:
																	break
													A_	,					A_       	=  bigram
													A_       	=  []
													A_       	=  0
													while i < len(a__						):
																	try:
																					A_       	=  word.index(a__       ,       a__						)
																	except ValueError:
																					new_word.extend(word[i:]						)
																					break
																	else:
																					new_word.extend(word[i:j]						)
																					A_       	=  j

																	if word[i] == first and i < len(a__						) - 1 and word[i + 1] == second:
																					new_word.append(first + second						)
																					i += 2
																	else:
																					new_word.append(word[i]						)
																					i += 1
													A_       	=  tuple(a__						)
													A_       	=  new_word
													if len(a__						) == 1:
																	break
													else:
																	A_       	=  get_pairs(a__						)
									A_       	=  ''' '''.join(a__						)
									A_       	=  word
									return word


					def        lowerCAmelCase_    (	self       ,       a__						)		->							Optional[int]:





									'''simple docstring'''
									A_       	=  []
									for token in re.findall(self.pat       ,       a__						):
													A_       	=  ''''''.join(
													    self.byte_encoder[b] for b in token.encode('''utf-8'''						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
													bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a__						).split(''' '''						)						)
									return bpe_tokens


					def        lowerCAmelCase_    (	self       ,       a__						)		->							Optional[Any]:





									'''simple docstring'''
									return self.encoder.get(a__       ,       self.encoder.get(self.unk_token						)						)


					def        lowerCAmelCase_    (	self       ,       a__						)		->							Any:





									'''simple docstring'''
									return self.decoder.get(a__						)


					def        lowerCAmelCase_    (	self       ,       a__						)		->							Optional[Any]:





									'''simple docstring'''
									A_       	=  ''''''.join(a__						)
									A_       	=  bytearray([self.byte_decoder[c] for c in text]						).decode('''utf-8'''       ,       errors=self.errors						)
									return text


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None						)		->							Tuple[str]:





									'''simple docstring'''
									if not os.path.isdir(a__						):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
													return
									A_       	=  os.path.join(
									    a__       ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)
									A_       	=  os.path.join(
									    a__       ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']						)

									with open(a__       ,       '''w'''       ,       encoding='''utf-8'''						) as f:
													f.write(json.dumps(self.encoder       ,       indent=2       ,       sort_keys=a__       ,       ensure_ascii=a__						) + '''\n'''						)

									A_       	=  0
									with open(a__       ,       '''w'''       ,       encoding='''utf-8'''						) as writer:
													writer.write('''#version: 0.2\n'''						)
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,       key=lambda a__						: kv[1]						):
																	if index != token_index:
																					logger.warning(
																					    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																					    ''' Please check that the tokenizer is not corrupted!'''						)
																					A_       	=  token_index
																	writer.write(''' '''.join(a__						) + '''\n'''						)
																	index += 1

									return vocab_file, merge_file


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None						)		->							List[int]:





									'''simple docstring'''
									if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									A_       	=  [self.cls_token_id]
									A_       	=  [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None       ,       a__ = False						)		->							List[int]:





									'''simple docstring'''
									if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=a__       ,       token_ids_a=a__       ,       already_has_special_tokens=a__						)

									if token_ids_a is None:
													return [1] + ([0] * len(a__						)) + [1]
									return [1] + ([0] * len(a__						)) + [1, 1] + ([0] * len(a__						)) + [1]


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None						)		->							List[int]:





									'''simple docstring'''
									A_       	=  [self.sep_token_id]
									A_       	=  [self.cls_token_id]

									if token_ids_a is None:
													return len(cls + token_ids_a + sep						) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__=False       ,       **a__						)		->							List[Any]:





									'''simple docstring'''
									A_       	=  kwargs.pop('''add_prefix_space'''       ,       self.add_prefix_space						)
									if (is_split_into_words or add_prefix_space) and (len(a__						) > 0 and not text[0].isspace()):
													A_       	=  ''' ''' + text
									return (text, kwargs)

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






@skip_mps
class 					__lowercase		(					A							,       A							,       A							,       unittest.TestCase       ):
					__magic_name__						:							List[str]     	=	StableDiffusionModelEditingPipeline
					__magic_name__						:							List[str]     	=	TEXT_TO_IMAGE_PARAMS
					__magic_name__						:							Any     	=	TEXT_TO_IMAGE_BATCH_PARAMS
					__magic_name__						:							Optional[Any]     	=	TEXT_TO_IMAGE_IMAGE_PARAMS
					__magic_name__						:							Tuple     	=	TEXT_TO_IMAGE_IMAGE_PARAMS


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





									'''simple docstring'''
									torch.manual_seed(0						)
									A_       	=  UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)       ,       layers_per_block=2       ,       sample_size=3_2       ,       in_channels=4       ,       out_channels=4       ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,       cross_attention_dim=3_2       ,       )
									A_       	=  DDIMScheduler()
									torch.manual_seed(0						)
									A_       	=  AutoencoderKL(
									    block_out_channels=[3_2, 6_4]       ,       in_channels=3       ,       out_channels=3       ,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,       latent_channels=4       ,       )
									torch.manual_seed(0						)
									A_       	=  CLIPTextConfig(
									    bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=3_2       ,       intermediate_size=3_7       ,       layer_norm_eps=1E-05       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_0_0_0       ,       )
									A_       	=  CLIPTextModel(a__						)
									A_       	=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

									A_       	=  {
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__=0						)		->							List[Any]:





									'''simple docstring'''
									A_       	=  torch.manual_seed(a__						)
									A_       	=  {
									    '''prompt''': '''A field of roses''',
									    '''generator''': generator,
									    # Setting height and width to None to prevent OOMs on CPU.
									    '''height''': None,
									    '''width''': None,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									}
									return inputs


					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									A_       	=  sd_pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1]
									assert image.shape == (1, 6_4, 6_4, 3)

									A_       	=  np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									A_       	=  '''french fries'''
									A_       	=  sd_pipe(**a__       ,       negative_prompt=a__						)
									A_       	=  output.images
									A_       	=  image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									A_       	=  np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  EulerAncestralDiscreteScheduler(
									    beta_start=0.0_00_85       ,       beta_end=0.0_12       ,       beta_schedule='''scaled_linear'''						)
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									A_       	=  sd_pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									A_       	=  np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  PNDMScheduler()
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									# the pipeline does not expect pndm so test if it raises error.
									with self.assertRaises(a__						):
													A_       	=  sd_pipe(**a__						).images


					def        lowerCAmelCase_    (	self						)		->							List[str]:





									'''simple docstring'''
									super().test_inference_batch_single_identical(expected_max_diff=5E-3						)


					def        lowerCAmelCase_    (	self						)		->							List[str]:





									'''simple docstring'''
									super().test_attention_slicing_forward_pass(expected_max_diff=5E-3						)






@slow
@require_torch_gpu
class 					__lowercase		(					unittest.TestCase       ):


					def        lowerCAmelCase_    (	self						)		->							List[Any]:





									'''simple docstring'''
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


					def        lowerCAmelCase_    (	self       ,       a__=0						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  torch.manual_seed(a__						)
									A_       	=  {
									    '''prompt''': '''A field of roses''',
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs


					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  '''CompVis/stable-diffusion-v1-4'''
									A_       	=  StableDiffusionModelEditingPipeline.from_pretrained(a__       ,       safety_checker=a__						)
									pipe.to(a__						)
									pipe.set_progress_bar_config(disable=a__						)
									pipe.enable_attention_slicing()

									A_       	=  self.get_inputs()
									A_       	=  pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)

									A_       	=  np.array(
									    [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]						)

									assert np.abs(expected_slice - image_slice						).max() < 1E-2

									# make sure image changes after editing
									pipe.edit_model('''A pack of roses'''       ,       '''A pack of blue roses'''						)

									A_       	=  pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)

									assert np.abs(expected_slice - image_slice						).max() > 1E-1


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





									'''simple docstring'''
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									A_       	=  '''CompVis/stable-diffusion-v1-4'''
									A_       	=  DDIMScheduler.from_pretrained(a__       ,       subfolder='''scheduler'''						)
									A_       	=  StableDiffusionModelEditingPipeline.from_pretrained(
									    a__       ,       scheduler=a__       ,       safety_checker=a__						)
									A_       	=  pipe.to(a__						)
									pipe.set_progress_bar_config(disable=a__						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

									A_       	=  self.get_inputs()
									A_       	=  pipe(**a__						)

									A_       	=  torch.cuda.max_memory_allocated()
									# make sure that less than 4.4 GB is allocated
									assert mem_bytes < 4.4 * 1_0**9