





import os
import unicodedata
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


UpperCamelCase_									=logging.get_logger(__name__)
UpperCamelCase_									={"""vocab_file""": """spiece.model"""}

UpperCamelCase_									={
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

UpperCamelCase_									={
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

UpperCamelCase_									="""▁"""




class 			_a						(				a_    ):
				UpperCamelCase								=   VOCAB_FILES_NAMES
				UpperCamelCase								=   PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase								=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






				def __init__(			self		:   Tuple,			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   List[Any]=True,			lowerCAmelCase__		:   Optional[int]=True,			lowerCAmelCase__		:   List[str]=False,			lowerCAmelCase__		:   int="[CLS]",			lowerCAmelCase__		:   Tuple="[SEP]",			lowerCAmelCase__		:   Any="<unk>",			lowerCAmelCase__		:   Dict="[SEP]",			lowerCAmelCase__		:   str="<pad>",			lowerCAmelCase__		:   Optional[int]="[CLS]",			lowerCAmelCase__		:   Union[str, Any]="[MASK]",			lowerCAmelCase__		:   str = None,			**lowerCAmelCase__		:   Union[str, Any],			)     ->				None:




										'''simple docstring'''



										_UpperCamelCase      : int         =   (
										    AddedToken(lowercase_,			lstrip=lowercase_,			rstrip=lowercase_,			normalized=lowercase_							)
										    if isinstance(lowercase_,			lowercase_							)
										    else mask_token
										)

										_UpperCamelCase      : Tuple         =   {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    do_lower_case=lowercase_,			remove_space=lowercase_,			keep_accents=lowercase_,			bos_token=lowercase_,			eos_token=lowercase_,			unk_token=lowercase_,			sep_token=lowercase_,			pad_token=lowercase_,			cls_token=lowercase_,			mask_token=lowercase_,			sp_model_kwargs=self.sp_model_kwargs,			**lowercase_,			)

										_UpperCamelCase      : Optional[int]         =   do_lower_case
										_UpperCamelCase      : Any         =   remove_space
										_UpperCamelCase      : Union[str, Any]         =   keep_accents
										_UpperCamelCase      : Optional[Any]         =   vocab_file

										_UpperCamelCase      : Optional[int]         =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(lowercase_							)






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




										'''simple docstring'''



										return len(self.sp_model							)






				def     snake_case     (			self		:   Dict							)     ->				List[str]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   {self.convert_ids_to_tokens(lowercase_							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab






				def __getstate__(			self		:   List[str]							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.__dict__.copy()
										_UpperCamelCase      : Union[str, Any]         =   None
										return state






				def __setstate__(			self		:   Union[str, Any],			lowerCAmelCase__		:   Optional[Any]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   d

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

										_UpperCamelCase      : Optional[int]         =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(self.vocab_file							)






				def     snake_case     (			self		:   List[str],			lowerCAmelCase__		:   Optional[int]							)     ->				Any:




										'''simple docstring'''



										if self.remove_space:
																_UpperCamelCase      : str         =   ''' '''.join(inputs.strip().split()							)
										else:
																_UpperCamelCase      : str         =   inputs
										_UpperCamelCase      : Optional[Any]         =   outputs.replace('''``''',			'''"'''							).replace('''\'\'''',			'''"'''							)

										if not self.keep_accents:
																_UpperCamelCase      : List[Any]         =   unicodedata.normalize('''NFKD''',			lowercase_							)
																_UpperCamelCase      : List[Any]         =   ''''''.join([c for c in outputs if not unicodedata.combining(lowercase_							)]							)
										if self.do_lower_case:
																_UpperCamelCase      : List[Any]         =   outputs.lower()

										return outputs






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   Union[str, Any]							)     ->				List[str]:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   self.preprocess_text(lowercase_							)
										_UpperCamelCase      : List[str]         =   self.sp_model.encode(lowercase_,			out_type=lowercase_							)
										_UpperCamelCase      : List[str]         =   []
										for piece in pieces:
																if len(lowercase_							) > 1 and piece[-1] == str(''','''							) and piece[-2].isdigit():
																						_UpperCamelCase      : Any         =   self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase_,			''''''							)							)
																						if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																												if len(cur_pieces[0]							) == 1:
																																		_UpperCamelCase      : Optional[int]         =   cur_pieces[1:]
																												else:
																																		_UpperCamelCase      : Any         =   cur_pieces[0][1:]
																						cur_pieces.append(piece[-1]							)
																						new_pieces.extend(lowercase_							)
																else:
																						new_pieces.append(lowercase_							)

										return new_pieces






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   Any							)     ->				List[str]:




										'''simple docstring'''



										return self.sp_model.PieceToId(lowercase_							)






				def     snake_case     (			self		:   Tuple,			lowerCAmelCase__		:   List[str]							)     ->				Any:




										'''simple docstring'''



										return self.sp_model.IdToPiece(lowercase_							)






				def     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   Union[str, Any]							)     ->				List[str]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   []
										_UpperCamelCase      : List[Any]         =   ''''''
										_UpperCamelCase      : Optional[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(lowercase_							) + token
																						_UpperCamelCase      : Any         =   True
																						_UpperCamelCase      : Optional[Any]         =   []
																else:
																						current_sub_tokens.append(lowercase_							)
																						_UpperCamelCase      : Any         =   False
										out_string += self.sp_model.decode(lowercase_							)
										return out_string.strip()






				def     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   str = None							)     ->				List[int]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   [self.sep_token_id]
										_UpperCamelCase      : Optional[Any]         =   [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		:   Dict,			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   Optional[Any] = None,			lowerCAmelCase__		:   str = False							)     ->				List[int]:




										'''simple docstring'''



										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=lowercase_,			token_ids_a=lowercase_,			already_has_special_tokens=lowercase_							)

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






				def     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   str = None							)     ->				List[int]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   [self.sep_token_id]
										_UpperCamelCase      : Optional[int]         =   [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		:   List[Any],			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   Optional[int] = None							)     ->				Tuple[str]:




										'''simple docstring'''



										if not os.path.isdir(lowercase_							):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
																return
										_UpperCamelCase      : str         =   os.path.join(
										    lowercase_,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

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

										return (out_vocab_file,)










"""simple docstring"""




import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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()




class 			_a						(				_lowerCAmelCase     ,    _lowerCAmelCase     ,    _lowerCAmelCase     ,    unittest.TestCase    ):
				UpperCamelCase								=   AltDiffusionPipeline
				UpperCamelCase								=   TEXT_TO_IMAGE_PARAMS
				UpperCamelCase								=   TEXT_TO_IMAGE_BATCH_PARAMS
				UpperCamelCase								=   TEXT_TO_IMAGE_IMAGE_PARAMS
				UpperCamelCase								=   TEXT_TO_IMAGE_IMAGE_PARAMS






				def     snake_case     (			self		:   int							)     ->				int:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase      : Dict         =   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,			)
										_UpperCamelCase      : Union[str, Any]         =   DDIMScheduler(
										    beta_start=0.00_085,			beta_end=0.012,			beta_schedule='''scaled_linear''',			clip_sample=lowerCAmelCase__,			set_alpha_to_one=lowerCAmelCase__,			)
										torch.manual_seed(0							)
										_UpperCamelCase      : List[str]         =   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,			)

										# TODO: address the non-deterministic text encoder (fails for save-load tests)
										# torch.manual_seed(0)
										# text_encoder_config = RobertaSeriesConfig(
										#     hidden_size=32,
										#     project_dim=32,
										#     intermediate_size=37,
										#     layer_norm_eps=1e-05,
										#     num_attention_heads=4,
										#     num_hidden_layers=5,
										#     vocab_size=5002,
										# )
										# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

										torch.manual_seed(0							)
										_UpperCamelCase      : str         =   CLIPTextConfig(
										    bos_token_id=0,			eos_token_id=2,			hidden_size=3_2,			projection_dim=3_2,			intermediate_size=3_7,			layer_norm_eps=1e-0_5,			num_attention_heads=4,			num_hidden_layers=5,			pad_token_id=1,			vocab_size=5_0_0_2,			)
										_UpperCamelCase      : List[Any]         =   CLIPTextModel(lowerCAmelCase__							)

										_UpperCamelCase      : Dict         =   XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''							)
										_UpperCamelCase      : str         =   7_7

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






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   int=0							)     ->				Optional[int]:




										'''simple docstring'''



										if str(lowerCAmelCase__							).startswith('''mps'''							):
																_UpperCamelCase      : Any         =   torch.manual_seed(lowerCAmelCase__							)
										else:
																_UpperCamelCase      : Dict         =   torch.Generator(device=lowerCAmelCase__							).manual_seed(lowerCAmelCase__							)
										_UpperCamelCase      : str         =   {
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 6.0,
										    '''output_type''': '''numpy''',
										}
										return inputs






				def     snake_case     (			self		:   List[Any]							)     ->				List[str]:




										'''simple docstring'''



										super().test_attention_slicing_forward_pass(expected_max_diff=3e-3							)






				def     snake_case     (			self		:   List[Any]							)     ->				Tuple:




										'''simple docstring'''



										super().test_inference_batch_single_identical(expected_max_diff=3e-3							)






				def     snake_case     (			self		:   List[str]							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator

										_UpperCamelCase      : int         =   self.get_dummy_components()
										torch.manual_seed(0							)
										_UpperCamelCase      : Any         =   RobertaSeriesConfig(
										    hidden_size=3_2,			project_dim=3_2,			intermediate_size=3_7,			layer_norm_eps=1e-0_5,			num_attention_heads=4,			num_hidden_layers=5,			vocab_size=5_0_0_2,			)
										# TODO: remove after fixing the non-deterministic text encoder
										_UpperCamelCase      : Tuple         =   RobertaSeriesModelWithTransformation(lowerCAmelCase__							)
										_UpperCamelCase      : str         =   text_encoder

										_UpperCamelCase      : List[Any]         =   AltDiffusionPipeline(**lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   alt_pipe.to(lowerCAmelCase__							)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[int]         =   self.get_dummy_inputs(lowerCAmelCase__							)
										_UpperCamelCase      : Optional[Any]         =   '''A photo of an astronaut'''
										_UpperCamelCase      : Any         =   alt_pipe(**lowerCAmelCase__							)
										_UpperCamelCase      : Any         =   output.images
										_UpperCamelCase      : Union[str, Any]         =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_UpperCamelCase      : List[Any]         =   np.array(
										    [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093]							)

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






				def     snake_case     (			self		:   int							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator

										_UpperCamelCase      : Optional[Any]         =   self.get_dummy_components()
										_UpperCamelCase      : str         =   PNDMScheduler(skip_prk_steps=lowerCAmelCase__							)
										torch.manual_seed(0							)
										_UpperCamelCase      : int         =   RobertaSeriesConfig(
										    hidden_size=3_2,			project_dim=3_2,			intermediate_size=3_7,			layer_norm_eps=1e-0_5,			num_attention_heads=4,			num_hidden_layers=5,			vocab_size=5_0_0_2,			)
										# TODO: remove after fixing the non-deterministic text encoder
										_UpperCamelCase      : Tuple         =   RobertaSeriesModelWithTransformation(lowerCAmelCase__							)
										_UpperCamelCase      : int         =   text_encoder
										_UpperCamelCase      : str         =   AltDiffusionPipeline(**lowerCAmelCase__							)
										_UpperCamelCase      : List[str]         =   alt_pipe.to(lowerCAmelCase__							)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : Union[str, Any]         =   self.get_dummy_inputs(lowerCAmelCase__							)
										_UpperCamelCase      : Optional[int]         =   alt_pipe(**lowerCAmelCase__							)
										_UpperCamelCase      : List[str]         =   output.images
										_UpperCamelCase      : Optional[int]         =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_UpperCamelCase      : List[Any]         =   np.array(
										    [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237]							)

										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]							)     ->				Any:




										'''simple docstring'''



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






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




										'''simple docstring'''



										_UpperCamelCase      : str         =   AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''',			safety_checker=lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   alt_pipe.to(lowerCAmelCase__							)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : int         =   '''A painting of a squirrel eating a burger'''
										_UpperCamelCase      : int         =   torch.manual_seed(0							)
										_UpperCamelCase      : Dict         =   alt_pipe([prompt],			generator=lowerCAmelCase__,			guidance_scale=6.0,			num_inference_steps=2_0,			output_type='''np'''							)

										_UpperCamelCase      : Optional[int]         =   output.images

										_UpperCamelCase      : Optional[int]         =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										_UpperCamelCase      : List[str]         =   np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586]							)

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






				def     snake_case     (			self		:   str							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''',			subfolder='''scheduler'''							)

										_UpperCamelCase      : Dict         =   AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''',			scheduler=lowerCAmelCase__,			safety_checker=lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   alt_pipe.to(lowerCAmelCase__							)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   '''A painting of a squirrel eating a burger'''
										_UpperCamelCase      : Optional[Any]         =   torch.manual_seed(0							)

										_UpperCamelCase      : Union[str, Any]         =   alt_pipe([prompt],			generator=lowerCAmelCase__,			num_inference_steps=2,			output_type='''numpy'''							)
										_UpperCamelCase      : Tuple         =   output.images

										_UpperCamelCase      : Dict         =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										_UpperCamelCase      : str         =   np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323]							)

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



