


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__  :Optional[Any]   			=							"▁"

lowerCAmelCase__  :Dict   			=							{"vocab_file": "spiece.model"}

lowerCAmelCase__  :int   			=							{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

lowerCAmelCase__  :Optional[Any]   			=							{
    "google/pegasus-xsum": 5_1_2,
}


lowerCAmelCase__  :Tuple   			=							logging.get_logger(__name__)

class 		__a      (				_a			):
		_a	:  List[Any]				=	VOCAB_FILES_NAMES

		_a	:  Any				=	VOCAB_FILES_NAMES
		_a	:  List[Any]				=	PRETRAINED_VOCAB_FILES_MAP
		_a	:  List[Any]				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a	:  int				=	["""input_ids""", """attention_mask"""]
		def __init__(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE="<pad>"			,    _SCREAMING_SNAKE_CASE="</s>"			,    _SCREAMING_SNAKE_CASE="<unk>"			,    _SCREAMING_SNAKE_CASE="<mask_2>"			,    _SCREAMING_SNAKE_CASE="<mask_1>"			,    _SCREAMING_SNAKE_CASE=None			,    _SCREAMING_SNAKE_CASE=103			,    _SCREAMING_SNAKE_CASE = None			,    **_SCREAMING_SNAKE_CASE			,    ) ->       None:




									"""simple docstring"""





									_UpperCAmelCase 		=			offset
									if additional_special_tokens is not None:
																if not isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						):
																							raise TypeError(
																							    f'''additional_special_tokens should be of type {type(_SCREAMING_SNAKE_CASE						)}, but is'''
																							    f''' {type(_SCREAMING_SNAKE_CASE						)}'''						)

																_UpperCAmelCase 		=			(
																    ([mask_token_sent] + additional_special_tokens)
																    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																    else additional_special_tokens
																)
																# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																additional_special_tokens_extended += [
																    f'''<unk_{i}>''' for i in range(len(_SCREAMING_SNAKE_CASE						)			,    self.offset - 1						)
																]

																if len(set(_SCREAMING_SNAKE_CASE						)						) != len(_SCREAMING_SNAKE_CASE						):
																							raise ValueError(
																							    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
																							    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''						)
																_UpperCAmelCase 		=			additional_special_tokens_extended
									else:
																_UpperCAmelCase 		=			[mask_token_sent] if mask_token_sent is not None else []
																additional_special_tokens += [f'''<unk_{i}>''' for i in range(2			,    self.offset						)]

									_UpperCAmelCase 		=			{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    eos_token=_SCREAMING_SNAKE_CASE			,    unk_token=_SCREAMING_SNAKE_CASE			,    mask_token=_SCREAMING_SNAKE_CASE			,    pad_token=_SCREAMING_SNAKE_CASE			,    mask_token_sent=_SCREAMING_SNAKE_CASE			,    offset=_SCREAMING_SNAKE_CASE			,    additional_special_tokens=_SCREAMING_SNAKE_CASE			,    sp_model_kwargs=self.sp_model_kwargs			,    **_SCREAMING_SNAKE_CASE			,    )
									_UpperCAmelCase 		=			mask_token_sent
									_UpperCAmelCase 		=			vocab_file
									_UpperCAmelCase 		=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
									self.sp_model.Load(_SCREAMING_SNAKE_CASE						)

									# add special tokens to encoder dict
									_UpperCAmelCase 		=			{
									    0: self.pad_token,
									    1: self.eos_token,
									}

									if self.mask_token_sent is not None:
																self.encoder.update(
																    {
																        2: self.mask_token_sent,
																        3: self.mask_token,
																    }						)

									if self.offset > 0:
																# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																# mask_token_sent is already added to list -> so start at 1
																self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,    self.offset - 1						)}						)

									_UpperCAmelCase 		=			{v: k for k, v in self.encoder.items()}
		@property
		def 	UpperCAmelCase__				(			self						) ->       int:




									"""simple docstring"""





									return len(self.sp_model						) + self.offset
		def 	UpperCAmelCase__				(			self						) ->       Dict[str, int]:




									"""simple docstring"""





									_UpperCAmelCase 		=			{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 __getstate__(			self						) ->       Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.__dict__.copy()
									_UpperCAmelCase 		=			None
									return state
		def __setstate__(			self			,    _SCREAMING_SNAKE_CASE						) ->       Dict:




									"""simple docstring"""





									_UpperCAmelCase 		=			d

									# for backward compatibility
									if not hasattr(self			,    'sp_model_kwargs'						):
																_UpperCAmelCase 		=			{}

									_UpperCAmelCase 		=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
									self.sp_model.Load(self.vocab_file						)
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE						) ->       List[str]:




									"""simple docstring"""





									return self.sp_model.encode(_SCREAMING_SNAKE_CASE			,    out_type=_SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE						) ->       int:




									"""simple docstring"""





									if token in self.decoder:
																return self.decoder[token]
									elif token in self.added_tokens_decoder:
																return self.added_tokens_decoder[token]
									_UpperCAmelCase 		=			self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE						)
									return sp_id + self.offset
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE						) ->       str:




									"""simple docstring"""





									if index in self.encoder:
																return self.encoder[index]
									elif index in self.added_tokens_encoder:
																return self.added_tokens_encoder[index]
									else:
																_UpperCAmelCase 		=			self.sp_model.IdToPiece(index - self.offset						)
									return token
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE						) ->       Any:




									"""simple docstring"""





									_UpperCAmelCase 		=			[]
									_UpperCAmelCase 		=			''
									for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																							out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE						) + token
																							_UpperCAmelCase 		=			[]
																else:
																							current_sub_tokens.append(_SCREAMING_SNAKE_CASE						)
									out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE						)
									return out_string.strip()
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE=False						) ->       Dict:




									"""simple docstring"""





									return 1
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE						) ->       str:




									"""simple docstring"""





									_UpperCAmelCase 		=			set(self.all_special_ids						)  # call it once instead of inside list comp
									all_special_ids.remove(self.unk_token_id						)  # <unk> is only sometimes special

									return [1 if x in all_special_ids else 0 for x in seq]
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE = None			,    _SCREAMING_SNAKE_CASE = False						) ->       List[int]:




									"""simple docstring"""





									if already_has_special_tokens:
																return self._special_token_mask(_SCREAMING_SNAKE_CASE						)
									elif token_ids_a is None:
																return self._special_token_mask(_SCREAMING_SNAKE_CASE						) + [1]
									else:
																return self._special_token_mask(token_ids_a + token_ids_a						) + [1]
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE=None						) ->       List[int]:




									"""simple docstring"""





									if token_ids_a is None:
																return token_ids_a + [self.eos_token_id]
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return token_ids_a + token_ids_a + [self.eos_token_id]






		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE = None						) ->       Tuple[str]:




									"""simple docstring"""





									if not os.path.isdir(_SCREAMING_SNAKE_CASE						):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
																return
									_UpperCAmelCase 		=			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:
																							_UpperCAmelCase 		=			self.sp_model.serialized_model_proto()
																							fi.write(_SCREAMING_SNAKE_CASE						)

									return (out_vocab_file,)




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 _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		1
           _lowercase       :							Any      		=		3
           _lowercase       :							Tuple      		=		(32, 32)

           _lowercase       :							Tuple      		=		floats_tensor((batch_size, num_channels) + sizes,      rng=random.Random(0)).to(lowerCamelCase)
           return image
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Dict      		=		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 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							str      		=		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 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Optional[int]      		=		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=50_06,      )
           return RobertaSeriesModelWithTransformation(lowerCamelCase)
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           def extract(*lowerCamelCase,      **lowerCamelCase):






               class _lowerCamelCase:
                      def __init__(					self)       ->   Optional[Any]:




                          """simple docstring"""

                          _lowercase       :							Optional[int]      		=		torch.ones([0])




                      def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   int:




                          """simple docstring"""

                          self.pixel_values.to(lowerCamelCase)
                          return self


               return Out()

           return extract
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

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

           _lowercase       :							int      		=		self.dummy_image.to(lowerCamelCase)
           _lowercase       :							int      		=		init_image / 2 + 0.5

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

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

           _lowercase       :							Optional[int]      		=		output.images

           _lowercase       :							Optional[Any]      		=		torch.Generator(device=lowerCamelCase).manual_seed(0)
           _lowercase       :							Optional[Any]      		=		alt_pipe(
               [prompt],      generator=lowerCamelCase,      guidance_scale=6.0,      num_inference_steps=2,      output_type='np',      image=lowerCamelCase,      return_dict=lowerCamelCase,      )[0]

           _lowercase       :							Optional[int]      		=		image[0, -3:, -3:, -1]
           _lowercase       :							Dict      		=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           _lowercase       :							int      		=		np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9])

           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 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.dummy_cond_unet
           _lowercase       :							Tuple      		=		PNDMScheduler(skip_prk_steps=lowerCamelCase)
           _lowercase       :							str      		=		self.dummy_vae
           _lowercase       :							Optional[Any]      		=		self.dummy_text_encoder
           _lowercase       :							Optional[Any]      		=		XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
           _lowercase       :							Optional[Any]      		=		77

           _lowercase       :							str      		=		self.dummy_image.to(lowerCamelCase)

           # put models in fp16
           _lowercase       :							List[str]      		=		unet.half()
           _lowercase       :							List[Any]      		=		vae.half()
           _lowercase       :							Any      		=		bert.half()

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

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

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




       @unittest.skipIf(torch_device != 'cuda',      'This test requires a GPU')
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							int      		=		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       :							str      		=		init_image.resize((7_60, 5_04))

           _lowercase       :							Optional[int]      		=		'BAAI/AltDiffusion'
           _lowercase       :							str      		=		AltDiffusionImgaImgPipeline.from_pretrained(
               lowerCamelCase,      safety_checker=lowerCamelCase,      )
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           pipe.enable_attention_slicing()

           _lowercase       :							List[str]      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							Any      		=		torch.manual_seed(0)
           _lowercase       :							Dict      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      strength=0.7_5,      guidance_scale=7.5,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							List[str]      		=		output.images[0]

           _lowercase       :							Tuple      		=		image[2_55:2_58, 3_83:3_86, -1]

           assert image.shape == (5_04, 7_60, 3)
           _lowercase       :							Optional[Any]      		=		np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0])

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






@slow
@require_torch_gpu
class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()




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




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           _lowercase       :							str      		=		init_image.resize((7_68, 5_12))
           _lowercase       :							Any      		=		load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy')

           _lowercase       :							str      		=		'BAAI/AltDiffusion'
           _lowercase       :							Optional[Any]      		=		AltDiffusionImgaImgPipeline.from_pretrained(
               lowerCamelCase,      safety_checker=lowerCamelCase,      )
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           pipe.enable_attention_slicing()

           _lowercase       :							int      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							List[Any]      		=		torch.manual_seed(0)
           _lowercase       :							int      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      strength=0.7_5,      guidance_scale=7.5,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							Union[str, Any]      		=		output.images[0]

           assert image.shape == (5_12, 7_68, 3)
           # img2img is flaky across GPUs even in fp32, so using MAE here
           assert np.abs(expected_image - image).max() < 1E-2


