




"""simple docstring"""

import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


__A       						=	logging.get_logger(__name__)

__A       						=	{"vocab_file": "spiece.model"}

__A       						=	{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

__A       						=	{
    "google/bigbird-roberta-base": 4_096,
    "google/bigbird-roberta-large": 4_096,
    "google/bigbird-base-trivia-itc": 4_096,
}
class 	lowerCamelCase__			(     SCREAMING_SNAKE_CASE__	):
			a__       :					Dict	    =   VOCAB_FILES_NAMES
			a__       :					Dict	    =   PRETRAINED_VOCAB_FILES_MAP
			a__       :					Tuple	    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a__       :					Union[str, Any]	    =   ['''input_ids''', '''attention_mask''']
			a__       :					List[int]	    =   []


			def __init__(   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE="<unk>" ,       SCREAMING_SNAKE_CASE="<s>" ,       SCREAMING_SNAKE_CASE="</s>" ,       SCREAMING_SNAKE_CASE="<pad>" ,       SCREAMING_SNAKE_CASE="[SEP]" ,       SCREAMING_SNAKE_CASE="[MASK]" ,       SCREAMING_SNAKE_CASE="[CLS]" ,       SCREAMING_SNAKE_CASE = None ,       **SCREAMING_SNAKE_CASE ,       ):



					"""simple docstring"""







					snake_case						:  List[Any]    	= AddedToken(A__ ,       lstrip=A__ ,       rstrip=A__							) if isinstance(A__ ,       A__							) else bos_token
					snake_case						:  Any    	= AddedToken(A__ ,       lstrip=A__ ,       rstrip=A__							) if isinstance(A__ ,       A__							) else eos_token
					snake_case						:  List[Any]    	= AddedToken(A__ ,       lstrip=A__ ,       rstrip=A__							) if isinstance(A__ ,       A__							) else unk_token
					snake_case						:  Optional[int]    	= AddedToken(A__ ,       lstrip=A__ ,       rstrip=A__							) if isinstance(A__ ,       A__							) else pad_token
					snake_case						:  Union[str, Any]    	= AddedToken(A__ ,       lstrip=A__ ,       rstrip=A__							) if isinstance(A__ ,       A__							) else cls_token
					snake_case						:  str    	= AddedToken(A__ ,       lstrip=A__ ,       rstrip=A__							) if isinstance(A__ ,       A__							) else sep_token

					# Mask token behave like a normal word, i.e. include the space before it
					snake_case						:  List[Any]    	= AddedToken(A__ ,       lstrip=A__ ,       rstrip=A__							) if isinstance(A__ ,       A__							) else mask_token

					snake_case						:  Dict    	= {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=A__ ,       eos_token=A__ ,       unk_token=A__ ,       pad_token=A__ ,       sep_token=A__ ,       mask_token=A__ ,       cls_token=A__ ,       sp_model_kwargs=self.sp_model_kwargs ,       **A__ ,       )

					snake_case						:  Tuple    	= vocab_file

					snake_case						:  Dict    	= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
					self.sp_model.Load(A__							)


			@property
			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					return self.sp_model.get_piece_size()


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Dict    	= {self.convert_ids_to_tokens(A__							): i for i in range(self.vocab_size							)}
					vocab.update(self.added_tokens_encoder							)
					return vocab


			def __getstate__(   self							):



					"""simple docstring"""







					snake_case						:  Dict    	= self.__dict__.copy()
					snake_case						:  Optional[int]    	= None
					return state


			def __setstate__(   self ,       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[str]    	= d

					# for backward compatibility
					if not hasattr(self ,       "sp_model_kwargs"							):
							snake_case						:  List[str]    	= {}

					snake_case						:  Dict    	= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
					self.sp_model.Load(self.vocab_file							)


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return self.sp_model.encode(A__ ,       out_type=A__							)


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return self.sp_model.piece_to_id(A__							)


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Tuple    	= self.sp_model.IdToPiece(A__							)
					return token


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Union[str, Any]    	= []
					snake_case						:  Tuple    	= ""
					snake_case						:  int    	= False
					for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
									if not prev_is_special:
											out_string += " "
									out_string += self.sp_model.decode(A__							) + token
									snake_case						:  str    	= True
									snake_case						:  List[str]    	= []
							else:
									current_sub_tokens.append(A__							)
									snake_case						:  Optional[Any]    	= False
					out_string += self.sp_model.decode(A__							)
					return out_string.strip()


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE = False ,       SCREAMING_SNAKE_CASE = None ,       SCREAMING_SNAKE_CASE = True ,       **SCREAMING_SNAKE_CASE ,       ):



					"""simple docstring"""







					snake_case						:  Tuple    	= kwargs.pop("use_source_tokenizer" ,       A__							)

					snake_case						:  Any    	= self.convert_ids_to_tokens(A__ ,       skip_special_tokens=A__							)

					# To avoid mixing byte-level and unicode for byte-level BPT
					# we need to build string separately for added tokens and byte-level tokens
					# cf. https://github.com/huggingface/transformers/issues/1133
					snake_case						:  List[Any]    	= []
					snake_case						:  str    	= []
					for token in filtered_tokens:
							if skip_special_tokens and token in self.all_special_ids:
									continue
							if token in self.added_tokens_encoder:
									if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(A__							)							)
											snake_case						:  Optional[Any]    	= []
									sub_texts.append(A__							)
							else:
									current_sub_text.append(A__							)
					if current_sub_text:
							sub_texts.append(self.convert_tokens_to_string(A__							)							)

					# Mimic the behavior of the Rust tokenizer:
					# No space before [MASK] and [SEP]
					if spaces_between_special_tokens:
							snake_case						:  Any    	= re.sub(r" (\[(MASK|SEP)\])" ,       r"\1" ,       " ".join(A__							)							)
					else:
							snake_case						:  Optional[Any]    	= "".join(A__							)

					snake_case						:  List[Any]    	= (
					    clean_up_tokenization_spaces
					    if clean_up_tokenization_spaces is not None
					    else self.clean_up_tokenization_spaces
					)
					if clean_up_tokenization_spaces:
							snake_case						:  List[str]    	= self.clean_up_tokenization(A__							)
							return clean_text
					else:
							return text


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE = None							):



					"""simple docstring"""







					if not os.path.isdir(A__							):
							logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
							return
					snake_case						:  List[Any]    	= os.path.join(
					    A__ ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

					if os.path.abspath(self.vocab_file							) != os.path.abspath(A__							) and os.path.isfile(self.vocab_file							):
							copyfile(self.vocab_file ,       A__							)
					elif not os.path.isfile(self.vocab_file							):
							with open(A__ ,       "wb"							) as fi:
									snake_case						:  List[Any]    	= self.sp_model.serialized_model_proto()
									fi.write(A__							)

					return (out_vocab_file,)


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE = None							):



					"""simple docstring"""







					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					snake_case						:  Dict    	= [self.cls_token_id]
					snake_case						:  Optional[Any]    	= [self.sep_token_id]
					return cls + token_ids_a + sep + token_ids_a + sep


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE = None ,       SCREAMING_SNAKE_CASE = False							):



					"""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] + ([0] * len(A__							)) + [1]



			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE = None							):



					"""simple docstring"""







					snake_case						:  int    	= [self.sep_token_id]
					snake_case						:  str    	= [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							) * [0] + len(token_ids_a + sep							) * [1]



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 				lowercase		(					unittest.TestCase		):




							def  A__      (				self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							@property
							def  A__      (				self):
												lowercase							=						1
												lowercase							=						3
												lowercase							=						(3_2, 3_2)

												lowercase							=						floats_tensor((batch_size, num_channels) + sizes   ,rng=random.Random(0)).to(A__)
												return image




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						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   ,)
												return model




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						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   ,)
												return model




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						RobertaSeriesConfig(
												    hidden_size=3_2   ,project_dim=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=5_0_0_6   ,)
												return RobertaSeriesModelWithTransformation(A__)




							@property
							def  A__      (				self):
												def extract(*A__   ,**A__):







																	class 				lowercase		:




																								def __init__(				self):
																													lowercase							=						torch.ones([0])





																								def  A__      (				self   ,A__):
																													self.pixel_values.to(A__)
																													return self


																	return Out()

												return extract




							def  A__      (				self):
												lowercase							=						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
												lowercase							=						self.dummy_cond_unet
												lowercase							=						PNDMScheduler(skip_prk_steps=A__)
												lowercase							=						self.dummy_vae
												lowercase							=						self.dummy_text_encoder
												lowercase							=						XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''')
												lowercase							=						7_7

												lowercase							=						self.dummy_image.to(A__)
												lowercase							=						init_image / 2 + 0.5

												# make sure here that pndm scheduler skips prk
												lowercase							=						AltDiffusionImgaImgPipeline(
												    unet=A__   ,scheduler=A__   ,vae=A__   ,text_encoder=A__   ,tokenizer=A__   ,safety_checker=A__   ,feature_extractor=self.dummy_extractor   ,)
												lowercase							=						VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,do_normalize=A__)
												lowercase							=						alt_pipe.to(A__)
												alt_pipe.set_progress_bar_config(disable=A__)

												lowercase							=						'''A painting of a squirrel eating a burger'''
												lowercase							=						torch.Generator(device=A__).manual_seed(0)
												lowercase							=						alt_pipe(
												    [prompt]   ,generator=A__   ,guidance_scale=6.0   ,num_inference_steps=2   ,output_type='''np'''   ,image=A__   ,)

												lowercase							=						output.images

												lowercase							=						torch.Generator(device=A__).manual_seed(0)
												lowercase							=						alt_pipe(
												    [prompt]   ,generator=A__   ,guidance_scale=6.0   ,num_inference_steps=2   ,output_type='''np'''   ,image=A__   ,return_dict=A__   ,)[0]

												lowercase							=						image[0, -3:, -3:, -1]
												lowercase							=						image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 3_2, 3_2, 3)
												lowercase							=						np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499])

												assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-3
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5E-3




							@unittest.skipIf(torch_device != '''cuda'''   ,'''This test requires a GPU''')
							def  A__      (				self):
												lowercase							=						self.dummy_cond_unet
												lowercase							=						PNDMScheduler(skip_prk_steps=A__)
												lowercase							=						self.dummy_vae
												lowercase							=						self.dummy_text_encoder
												lowercase							=						XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''')
												lowercase							=						7_7

												lowercase							=						self.dummy_image.to(A__)

												# put models in fp16
												lowercase							=						unet.half()
												lowercase							=						vae.half()
												lowercase							=						bert.half()

												# make sure here that pndm scheduler skips prk
												lowercase							=						AltDiffusionImgaImgPipeline(
												    unet=A__   ,scheduler=A__   ,vae=A__   ,text_encoder=A__   ,tokenizer=A__   ,safety_checker=A__   ,feature_extractor=self.dummy_extractor   ,)
												lowercase							=						VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,do_normalize=A__)
												lowercase							=						alt_pipe.to(A__)
												alt_pipe.set_progress_bar_config(disable=A__)

												lowercase							=						'''A painting of a squirrel eating a burger'''
												lowercase							=						torch.manual_seed(0)
												lowercase							=						alt_pipe(
												    [prompt]   ,generator=A__   ,num_inference_steps=2   ,output_type='''np'''   ,image=A__   ,).images

												assert image.shape == (1, 3_2, 3_2, 3)





							@unittest.skipIf(torch_device != '''cuda'''   ,'''This test requires a GPU''')
							def  A__      (				self):
												lowercase							=						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/img2img/sketch-mountains-input.jpg''')
												# resize to resolution that is divisible by 8 but not 16 or 32
												lowercase							=						init_image.resize((7_6_0, 5_0_4))

												lowercase							=						'''BAAI/AltDiffusion'''
												lowercase							=						AltDiffusionImgaImgPipeline.from_pretrained(
												    A__   ,safety_checker=A__   ,)
												pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)
												pipe.enable_attention_slicing()

												lowercase							=						'''A fantasy landscape, trending on artstation'''

												lowercase							=						torch.manual_seed(0)
												lowercase							=						pipe(
												    prompt=A__   ,image=A__   ,strength=0.75   ,guidance_scale=7.5   ,generator=A__   ,output_type='''np'''   ,)
												lowercase							=						output.images[0]

												lowercase							=						image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

												assert image.shape == (5_0_4, 7_6_0, 3)
												lowercase							=						np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000])

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







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




							def  A__      (				self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





							def  A__      (				self):
												lowercase							=						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/img2img/sketch-mountains-input.jpg''')
												lowercase							=						init_image.resize((7_6_8, 5_1_2))
												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy''')

												lowercase							=						'''BAAI/AltDiffusion'''
												lowercase							=						AltDiffusionImgaImgPipeline.from_pretrained(
												    A__   ,safety_checker=A__   ,)
												pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)
												pipe.enable_attention_slicing()

												lowercase							=						'''A fantasy landscape, trending on artstation'''

												lowercase							=						torch.manual_seed(0)
												lowercase							=						pipe(
												    prompt=A__   ,image=A__   ,strength=0.75   ,guidance_scale=7.5   ,generator=A__   ,output_type='''np'''   ,)
												lowercase							=						output.images[0]

												assert image.shape == (5_1_2, 7_6_8, 3)
												# img2img is flaky across GPUs even in fp32, so using MAE here
												assert np.abs(expected_image - image).max() < 1E-2




