






'''simple docstring'''







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 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Union[str, Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			int:
							a_		:  Tuple											=      1
							a_		:  Optional[Any]											=      3
							a_		:  List[Any]											=      (32, 32)

							a_		:  Optional[Any]											=      floats_tensor((batch_size, num_channels) + sizes  ,						rng=random.Random(0					)					).to(__SCREAMING_SNAKE_CASE					)
							return image






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			int:
							torch.manual_seed(0					)
							a_		:  Optional[Any]											=      UNetaDConditionModel(
							    block_out_channels=(32, 64)  ,						layers_per_block=2  ,						sample_size=32  ,						in_channels=4  ,						out_channels=4  ,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,						cross_attention_dim=32  ,						)
							return model






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			Any:
							torch.manual_seed(0					)
							a_		:  List[Any]											=      AutoencoderKL(
							    block_out_channels=[32, 64]  ,						in_channels=3  ,						out_channels=3  ,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']  ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']  ,						latent_channels=4  ,						)
							return model






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			str:
							torch.manual_seed(0					)
							a_		:  Tuple											=      RobertaSeriesConfig(
							    hidden_size=32  ,						project_dim=32  ,						intermediate_size=37  ,						layer_norm_eps=1e-05  ,						num_attention_heads=4  ,						num_hidden_layers=5  ,						pad_token_id=1  ,						vocab_size=5006  ,						)
							return RobertaSeriesModelWithTransformation(__SCREAMING_SNAKE_CASE					)






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Union[str, Any]:
							def extract(*__SCREAMING_SNAKE_CASE      :			List[Any]  ,						**__SCREAMING_SNAKE_CASE      :			Any					):





											class 	SCREAMING_SNAKE_CASE					:






														def __init__( self      :			Union[str, Any]					)			->			int:
																		a_		:  List[str]											=      torch.ones([0]					)







														def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Union[str, Any]:
																		self.pixel_values.to(__SCREAMING_SNAKE_CASE					)
																		return self





											return Out()

							return extract






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Tuple:
							a_		:  int											=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							a_		:  Optional[int]											=      self.dummy_cond_unet
							a_		:  Union[str, Any]											=      PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      self.dummy_vae
							a_		:  Optional[int]											=      self.dummy_text_encoder
							a_		:  Union[str, Any]											=      XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''					)
							a_		:  Optional[int]											=      77

							a_		:  int											=      self.dummy_image.to(__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      init_image / 2 + 0.5

							# make sure here that pndm scheduler skips prk
							a_		:  Optional[Any]											=      AltDiffusionImgaImgPipeline(
							    unet=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE  ,						vae=__SCREAMING_SNAKE_CASE  ,						text_encoder=__SCREAMING_SNAKE_CASE  ,						tokenizer=__SCREAMING_SNAKE_CASE  ,						safety_checker=__SCREAMING_SNAKE_CASE  ,						feature_extractor=self.dummy_extractor  ,						)
							a_		:  List[str]											=      VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor  ,						do_normalize=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      alt_pipe.to(__SCREAMING_SNAKE_CASE					)
							alt_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Dict											=      '''A painting of a squirrel eating a burger'''
							a_		:  str											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(0					)
							a_		:  Optional[Any]											=      alt_pipe(
							    [prompt]  ,						generator=__SCREAMING_SNAKE_CASE  ,						guidance_scale=6.0  ,						num_inference_steps=2  ,						output_type='''np'''  ,						image=__SCREAMING_SNAKE_CASE  ,						)

							a_		:  int											=      output.images

							a_		:  List[Any]											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(0					)
							a_		:  List[str]											=      alt_pipe(
							    [prompt]  ,						generator=__SCREAMING_SNAKE_CASE  ,						guidance_scale=6.0  ,						num_inference_steps=2  ,						output_type='''np'''  ,						image=__SCREAMING_SNAKE_CASE  ,						return_dict=__SCREAMING_SNAKE_CASE  ,						)[0]

							a_		:  Optional[Any]											=      image[0, -3:, -3:, -1]
							a_		:  Tuple											=      image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							a_		:  List[Any]											=      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 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Optional[Any]:
							a_		:  Optional[int]											=      self.dummy_cond_unet
							a_		:  List[Any]											=      PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE					)
							a_		:  int											=      self.dummy_vae
							a_		:  Optional[int]											=      self.dummy_text_encoder
							a_		:  Optional[int]											=      XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''					)
							a_		:  List[str]											=      77

							a_		:  Tuple											=      self.dummy_image.to(__SCREAMING_SNAKE_CASE					)

							# put models in fp16
							a_		:  str											=      unet.half()
							a_		:  int											=      vae.half()
							a_		:  str											=      bert.half()

							# make sure here that pndm scheduler skips prk
							a_		:  int											=      AltDiffusionImgaImgPipeline(
							    unet=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE  ,						vae=__SCREAMING_SNAKE_CASE  ,						text_encoder=__SCREAMING_SNAKE_CASE  ,						tokenizer=__SCREAMING_SNAKE_CASE  ,						safety_checker=__SCREAMING_SNAKE_CASE  ,						feature_extractor=self.dummy_extractor  ,						)
							a_		:  str											=      VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor  ,						do_normalize=__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      alt_pipe.to(__SCREAMING_SNAKE_CASE					)
							alt_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  str											=      '''A painting of a squirrel eating a burger'''
							a_		:  Any											=      torch.manual_seed(0					)
							a_		:  int											=      alt_pipe(
							    [prompt]  ,						generator=__SCREAMING_SNAKE_CASE  ,						num_inference_steps=2  ,						output_type='''np'''  ,						image=__SCREAMING_SNAKE_CASE  ,						).images

							assert image.shape == (1, 32, 32, 3)







			@unittest.skipIf(torch_device != '''cuda'''  ,						'''This test requires a GPU'''					)
			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			str:
							a_		:  List[Any]											=      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
							a_		:  List[str]											=      init_image.resize((760, 504)					)

							a_		:  str											=      '''BAAI/AltDiffusion'''
							a_		:  List[str]											=      AltDiffusionImgaImgPipeline.from_pretrained(
							    __SCREAMING_SNAKE_CASE  ,						safety_checker=__SCREAMING_SNAKE_CASE  ,						)
							pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)
							pipe.enable_attention_slicing()

							a_		:  Dict											=      '''A fantasy landscape, trending on artstation'''

							a_		:  Any											=      torch.manual_seed(0					)
							a_		:  str											=      pipe(
							    prompt=__SCREAMING_SNAKE_CASE  ,						image=__SCREAMING_SNAKE_CASE  ,						strength=0.75  ,						guidance_scale=7.5  ,						generator=__SCREAMING_SNAKE_CASE  ,						output_type='''np'''  ,						)
							a_		:  Optional[Any]											=      output.images[0]

							a_		:  Optional[int]											=      image[255:258, 383:386, -1]

							assert image.shape == (504, 760, 3)
							a_		:  Union[str, Any]											=      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 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			str:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			List[str]:
							a_		:  List[Any]											=      load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/img2img/sketch-mountains-input.jpg'''					)
							a_		:  Optional[int]											=      init_image.resize((768, 512)					)
							a_		:  Optional[int]											=      load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''					)

							a_		:  Union[str, Any]											=      '''BAAI/AltDiffusion'''
							a_		:  str											=      AltDiffusionImgaImgPipeline.from_pretrained(
							    __SCREAMING_SNAKE_CASE  ,						safety_checker=__SCREAMING_SNAKE_CASE  ,						)
							pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)
							pipe.enable_attention_slicing()

							a_		:  Optional[Any]											=      '''A fantasy landscape, trending on artstation'''

							a_		:  Union[str, Any]											=      torch.manual_seed(0					)
							a_		:  Tuple											=      pipe(
							    prompt=__SCREAMING_SNAKE_CASE  ,						image=__SCREAMING_SNAKE_CASE  ,						strength=0.75  ,						guidance_scale=7.5  ,						generator=__SCREAMING_SNAKE_CASE  ,						output_type='''np'''  ,						)
							a_		:  List[Any]											=      output.images[0]

							assert image.shape == (512, 768, 3)
							# img2img is flaky across GPUs even in fp32, so using MAE here
							assert np.abs(expected_image - image					).max() < 1e-2












'''simple docstring'''







import os
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


__lowerCAmelCase       		=      logging.get_logger(__name__)

__lowerCAmelCase       		=      {'vocab_file': 'sentencepiece.bpe.model'}

__lowerCAmelCase       		=      {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

__lowerCAmelCase       		=      {
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

__lowerCAmelCase       		=      '▁'





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				VOCAB_FILES_NAMES
			snake_case__  =				PRETRAINED_VOCAB_FILES_MAP
			snake_case__  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__  =				["input_ids", "attention_mask"]






			def __init__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Dict="<s>"  ,						__SCREAMING_SNAKE_CASE      :			List[Any]="</s>"  ,						__SCREAMING_SNAKE_CASE      :			List[str]="</s>"  ,						__SCREAMING_SNAKE_CASE      :			List[str]="<s>"  ,						__SCREAMING_SNAKE_CASE      :			Dict="<unk>"  ,						__SCREAMING_SNAKE_CASE      :			int="<pad>"  ,						__SCREAMING_SNAKE_CASE      :			Tuple="<mask>"  ,						__SCREAMING_SNAKE_CASE      :			Optional[Dict[str, Any]] = None  ,						**__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						)			->			None:
							# Mask token behave like a normal word, i.e. include the space before it
							a_		:  Tuple											=      AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					) if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else mask_token

							a_		:  List[str]											=      {} if sp_model_kwargs is None else sp_model_kwargs

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

							a_		:  Tuple											=      vocab_file
							a_		:  Optional[Any]											=      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(str(__SCREAMING_SNAKE_CASE					)					)

							a_		:  Optional[Any]											=      {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							a_		:  Any											=      len(self.sp_model					) - 1
							a_		:  Optional[Any]											=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:

							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							a_		:  List[str]											=      [self.cls_token_id]
							a_		:  Optional[Any]											=      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None  ,						__SCREAMING_SNAKE_CASE      :			bool = False					)			->			List[int]:
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=__SCREAMING_SNAKE_CASE  ,						token_ids_a=__SCREAMING_SNAKE_CASE  ,						already_has_special_tokens=__SCREAMING_SNAKE_CASE					)

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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:
							a_		:  List[str]											=      [self.sep_token_id]
							a_		:  Any											=      [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]






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Union[str, Any]:
							return len(self.sp_model					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Tuple:
							a_		:  int											=      {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			str					)			->			List[str]:
							return self.sp_model.encode(__SCREAMING_SNAKE_CASE  ,						out_type=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Dict:
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							a_		:  Optional[int]											=      self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE					)

							return spm_id if spm_id else self.unk_token_id






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			str:
							if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Optional[int]:
							a_		:  Dict											=      []
							a_		:  List[Any]											=      ''''''
							a_		:  Dict											=      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(__SCREAMING_SNAKE_CASE					) + token
															a_		:  Dict											=      True
															a_		:  Optional[Any]											=      []
											else:
															current_sub_tokens.append(__SCREAMING_SNAKE_CASE					)
															a_		:  Tuple											=      False
							out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE					)
							return out_string.strip()






			def __getstate__( self      :			Dict					)			->			int:
							a_		:  Dict											=      self.__dict__.copy()
							a_		:  List[str]											=      None
							return state






			def __setstate__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Any:
							a_		:  Optional[Any]											=      d

							# for backward compatibility
							if not hasattr(self  ,						'''sp_model_kwargs'''					):
											a_		:  Union[str, Any]											=      {}

							a_		:  int											=      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(self.vocab_file					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[str] = None					)			->			Tuple[str]:
							if not os.path.isdir(__SCREAMING_SNAKE_CASE					):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
											return
							a_		:  Union[str, Any]											=      os.path.join(
							    __SCREAMING_SNAKE_CASE  ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(__SCREAMING_SNAKE_CASE					) and os.path.isfile(self.vocab_file					):
											copyfile(self.vocab_file  ,						__SCREAMING_SNAKE_CASE					)
							elif not os.path.isfile(self.vocab_file					):
											with open(__SCREAMING_SNAKE_CASE  ,						'''wb'''					) as fi:
															a_		:  Any											=      self.sp_model.serialized_model_proto()
															fi.write(__SCREAMING_SNAKE_CASE					)

							return (out_vocab_file,)




