

'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_rembert import RemBertTokenizer
else:
      __lowerCamelCase						    =      None

__lowerCamelCase						    =      logging.get_logger(__name__)
__lowerCamelCase						    =      {'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase						    =      {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
    '''tokenizer_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
    },
}

__lowerCamelCase						    =      {
    '''google/rembert''': 256,
}

__lowerCamelCase						    =      '''▁'''


class    A__ ( _snake_case     ):
    lowercase		   =						VOCAB_FILES_NAMES
    lowercase		   =						PRETRAINED_VOCAB_FILES_MAP
    lowercase		   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase		   =						RemBertTokenizer



    def __init__(   self						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=True						,			UpperCamelCase__=True						,			UpperCamelCase__=False						,			UpperCamelCase__="[CLS]"						,			UpperCamelCase__="[SEP]"						,			UpperCamelCase__="<unk>"						,			UpperCamelCase__="[SEP]"						,			UpperCamelCase__="<pad>"						,			UpperCamelCase__="[CLS]"						,			UpperCamelCase__="[MASK]"						,			**UpperCamelCase__						,			)  -> Tuple:





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

           super().__init__(
               UpperCamelCase__						,			tokenizer_file=UpperCamelCase__						,			do_lower_case=UpperCamelCase__						,			remove_space=UpperCamelCase__						,			keep_accents=UpperCamelCase__						,			bos_token=UpperCamelCase__						,			eos_token=UpperCamelCase__						,			unk_token=UpperCamelCase__						,			sep_token=UpperCamelCase__						,			pad_token=UpperCamelCase__						,			cls_token=UpperCamelCase__						,			mask_token=UpperCamelCase__						,			**UpperCamelCase__						,			)

           A_		   = do_lower_case
           A_		   = remove_space
           A_		   = keep_accents
           A_		   = vocab_file
           A_		   = False if not self.vocab_file else True



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None    )  -> List[int]:





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



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = False    )  -> List[int]:





           '''simple docstring'''

           if already_has_special_tokens:
                  if token_ids_a is not None:
                         raise ValueError(
                             """You should not supply a second sequence if the provided sequence of """
                             """ids is already formatted with special tokens for the model."""    )
                  return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is not None:
                  return [1] + ([0] * len(UpperCamelCase__    )) + [1] + ([0] * len(UpperCamelCase__    )) + [1]
           return [1] + ([0] * len(UpperCamelCase__    )) + [1]



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = 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    ) * [0] + len(token_ids_a + sep    ) * [1]







    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None    )  -> Tuple[str]:





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

           if os.path.abspath(self.vocab_file    ) != os.path.abspath(UpperCamelCase__    ):
                  copyfile(self.vocab_file						,			UpperCamelCase__    )

           return (out_vocab_file,)



'''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    A__ ( unittest.TestCase     ):



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





           '''simple docstring'''
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



    @property
    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           A_		   = 1
           A_		   = 3
           A_		   = (32, 32)

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



    @property
    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = 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    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = 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    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = RobertaSeriesConfig(
               hidden_size=32						,			project_dim=32						,			intermediate_size=37						,			layer_norm_eps=1e-0_5						,			num_attention_heads=4						,			num_hidden_layers=5						,			pad_token_id=1						,			vocab_size=5006						,			)
           return RobertaSeriesModelWithTransformation(UpperCamelCase__    )



    @property
    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           def extract(*UpperCamelCase__						,			**UpperCamelCase__    ):


                  class    A__ :



                      def __init__(   self    )  -> Dict:





                             '''simple docstring'''
                             A_		   = torch.ones([0]    )







                      def    snake_case_     (   self						,			UpperCamelCase__    )  -> str:





                             '''simple docstring'''
                             self.pixel_values.to(UpperCamelCase__    )
                             return self

                  return Out()

           return extract



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





           '''simple docstring'''
           A_		   = """cpu"""  # ensure determinism for the device-dependent torch.Generator
           A_		   = self.dummy_cond_unet
           A_		   = PNDMScheduler(skip_prk_steps=UpperCamelCase__    )
           A_		   = self.dummy_vae
           A_		   = self.dummy_text_encoder
           A_		   = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""    )
           A_		   = 77

           A_		   = self.dummy_image.to(UpperCamelCase__    )
           A_		   = init_image / 2 + 0.5

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

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

           A_		   = output.images

           A_		   = torch.Generator(device=UpperCamelCase__    ).manual_seed(0    )
           A_		   = alt_pipe(
               [prompt]						,			generator=UpperCamelCase__						,			guidance_scale=6.0						,			num_inference_steps=2						,			output_type="""np"""						,			image=UpperCamelCase__						,			return_dict=UpperCamelCase__						,			)[0]

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

           assert image.shape == (1, 32, 32, 3)
           A_		   = 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    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = self.dummy_cond_unet
           A_		   = PNDMScheduler(skip_prk_steps=UpperCamelCase__    )
           A_		   = self.dummy_vae
           A_		   = self.dummy_text_encoder
           A_		   = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""    )
           A_		   = 77

           A_		   = self.dummy_image.to(UpperCamelCase__    )

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

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

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

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







    @unittest.skipIf(torch_device != """cuda"""						,			"""This test requires a GPU"""    )
    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = 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_		   = init_image.resize((760, 504)    )

           A_		   = """BAAI/AltDiffusion"""
           A_		   = AltDiffusionImgaImgPipeline.from_pretrained(
               UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			)
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )
           pipe.enable_attention_slicing()

           A_		   = """A fantasy landscape, trending on artstation"""

           A_		   = torch.manual_seed(0    )
           A_		   = pipe(
               prompt=UpperCamelCase__						,			image=UpperCamelCase__						,			strength=0.75						,			guidance_scale=7.5						,			generator=UpperCamelCase__						,			output_type="""np"""						,			)
           A_		   = output.images[0]

           A_		   = image[255:258, 383:386, -1]

           assert image.shape == (504, 760, 3)
           A_		   = 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    A__ ( unittest.TestCase     ):



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





           '''simple docstring'''
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







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





           '''simple docstring'''
           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""    )
           A_		   = init_image.resize((768, 512)    )
           A_		   = load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""    )

           A_		   = """BAAI/AltDiffusion"""
           A_		   = AltDiffusionImgaImgPipeline.from_pretrained(
               UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			)
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )
           pipe.enable_attention_slicing()

           A_		   = """A fantasy landscape, trending on artstation"""

           A_		   = torch.manual_seed(0    )
           A_		   = pipe(
               prompt=UpperCamelCase__						,			image=UpperCamelCase__						,			strength=0.75						,			guidance_scale=7.5						,			generator=UpperCamelCase__						,			output_type="""np"""						,			)
           A_		   = 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
