
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


_A     =  "▁"

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

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

_A     =  {
    "google/pegasus-xsum": 5_12,
}


_A     =  logging.get_logger(__name__)





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	=VOCAB_FILES_NAMES

				_lowercase    	=VOCAB_FILES_NAMES
				_lowercase    	=PRETRAINED_VOCAB_FILES_MAP
				_lowercase    	=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase    	=['''input_ids''', '''attention_mask''']




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase="<pad>"       ,       _UpperCamelCase="</s>"       ,       _UpperCamelCase="<unk>"       ,       _UpperCamelCase="<mask_2>"       ,       _UpperCamelCase="<mask_1>"       ,       _UpperCamelCase=None       ,       _UpperCamelCase=103       ,       _UpperCamelCase = None       ,       **_UpperCamelCase       ,       )       ->   None:
							lowerCAmelCase_      			=			offset
							if additional_special_tokens is not None:
										if not isinstance(_UpperCamelCase       ,       _UpperCamelCase  ):
													raise TypeError(
													    f"""additional_special_tokens should be of type {type(_UpperCamelCase  )}, but is"""
													    f""" {type(_UpperCamelCase  )}"""  )

										lowerCAmelCase_      			=			(
										    ([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(_UpperCamelCase  )       ,       self.offset - 1  )
										]

										if len(set(_UpperCamelCase  )  ) != len(_UpperCamelCase  ):
													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}."""  )
										lowerCAmelCase_      			=			additional_special_tokens_extended
							else:
										lowerCAmelCase_      			=			[mask_token_sent] if mask_token_sent is not None else []
										additional_special_tokens += [f"""<unk_{i}>""" for i in range(2       ,       self.offset  )]

							lowerCAmelCase_      			=			{} if sp_model_kwargs is None else sp_model_kwargs

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

							# add special tokens to encoder dict
							lowerCAmelCase_      			=			{
							    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  )}  )

							lowerCAmelCase_      			=			{v: k for k, v in self.encoder.items()}




				@property
				def   __a		(      self  )       ->   int:
							return len(self.sp_model  ) + self.offset




				def   __a		(      self  )       ->   Dict[str, int]:
							lowerCAmelCase_      			=			{self.convert_ids_to_tokens(_UpperCamelCase  ): i for i in range(self.vocab_size  )}
							vocab.update(self.added_tokens_encoder  )
							return vocab




				def __getstate__(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			self.__dict__.copy()
							lowerCAmelCase_      			=			None
							return state




				def __setstate__(      self       ,       _UpperCamelCase  )       ->   Optional[int]:
							lowerCAmelCase_      			=			d

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

							lowerCAmelCase_      			=			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(self.vocab_file  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   List[str]:
							return self.sp_model.encode(_UpperCamelCase       ,       out_type=_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
							if token in self.decoder:
										return self.decoder[token]
							elif token in self.added_tokens_decoder:
										return self.added_tokens_decoder[token]
							lowerCAmelCase_      			=			self.sp_model.piece_to_id(_UpperCamelCase  )
							return sp_id + self.offset




				def   __a		(      self       ,       _UpperCamelCase  )       ->   str:
							if index in self.encoder:
										return self.encoder[index]
							elif index in self.added_tokens_encoder:
										return self.added_tokens_encoder[index]
							else:
										lowerCAmelCase_      			=			self.sp_model.IdToPiece(index - self.offset  )
							return token




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			[]
							lowerCAmelCase_      			=			""
							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(_UpperCamelCase  ) + token
													lowerCAmelCase_      			=			[]
										else:
													current_sub_tokens.append(_UpperCamelCase  )
							out_string += self.sp_model.decode(_UpperCamelCase  )
							return out_string.strip()




				def   __a		(      self       ,       _UpperCamelCase=False  )       ->   Optional[int]:
							return 1




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




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None  )       ->   List[int]:
							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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   Tuple[str]:
							if not os.path.isdir(_UpperCamelCase  ):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
										return
							lowerCAmelCase_      			=			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  ) and os.path.isfile(self.vocab_file  ):
										copyfile(self.vocab_file       ,       _UpperCamelCase  )
							elif not os.path.isfile(self.vocab_file  ):
										with open(_UpperCamelCase       ,       "wb"  ) as fi:
													lowerCAmelCase_      			=			self.sp_model.serialized_model_proto()
													fi.write(_UpperCamelCase  )

							return (out_vocab_file,)





import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

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,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class 			_lowerCAmelCase      (			__a      ,   __a      ,   __a      ,   unittest.TestCase ):
				_lowercase    	=StableUnCLIPPipeline
				_lowercase    	=TEXT_TO_IMAGE_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_BATCH_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_IMAGE_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_IMAGE_PARAMS

				# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
				_lowercase    	=False




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			32
							lowerCAmelCase_      			=			embedder_hidden_size

							# prior components

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTextModelWithProjection(
							    CLIPTextConfig(
							        bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=_UpperCamelCase       ,       projection_dim=_UpperCamelCase       ,       intermediate_size=37       ,       layer_norm_eps=1e-0_5       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_000       ,       )  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			PriorTransformer(
							    num_attention_heads=2       ,       attention_head_dim=12       ,       embedding_dim=_UpperCamelCase       ,       num_layers=1       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			DDPMScheduler(
							    variance_type="fixed_small_log"       ,       prediction_type="sample"       ,       num_train_timesteps=1_000       ,       clip_sample=_UpperCamelCase       ,       clip_sample_range=5.0       ,       beta_schedule="squaredcos_cap_v2"       ,       )

							# regular denoising components

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			StableUnCLIPImageNormalizer(embedding_dim=_UpperCamelCase  )
							lowerCAmelCase_      			=			DDPMScheduler(beta_schedule="squaredcos_cap_v2"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=_UpperCamelCase       ,       projection_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=1_000       ,       )  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			UNetaDConditionModel(
							    sample_size=32       ,       in_channels=4       ,       out_channels=4       ,       down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")       ,       up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")       ,       block_out_channels=(32, 64)       ,       attention_head_dim=(2, 4)       ,       class_embed_type="projection"       ,       projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,       cross_attention_dim=_UpperCamelCase       ,       layers_per_block=1       ,       upcast_attention=_UpperCamelCase       ,       use_linear_projection=_UpperCamelCase       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			DDIMScheduler(
							    beta_schedule="scaled_linear"       ,       beta_start=0.00085       ,       beta_end=0.012       ,       prediction_type="v_prediction"       ,       set_alpha_to_one=_UpperCamelCase       ,       steps_offset=1       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			AutoencoderKL()

							lowerCAmelCase_      			=			{
							    # prior components
							    "prior_tokenizer": prior_tokenizer,
							    "prior_text_encoder": prior_text_encoder,
							    "prior": prior,
							    "prior_scheduler": prior_scheduler,
							    # image noising components
							    "image_normalizer": image_normalizer,
							    "image_noising_scheduler": image_noising_scheduler,
							    # regular denoising components
							    "tokenizer": tokenizer,
							    "text_encoder": text_encoder,
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							}

							return components




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=0  )       ->   Tuple:
							if str(_UpperCamelCase  ).startswith("mps"  ):
										lowerCAmelCase_      			=			torch.manual_seed(_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )
							lowerCAmelCase_      			=			{
							    "prompt": "A painting of a squirrel eating a burger",
							    "generator": generator,
							    "num_inference_steps": 2,
							    "prior_num_inference_steps": 2,
							    "output_type": "numpy",
							}
							return inputs




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			torch_device == "cpu"

							self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase  )




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			torch_device in ["cpu", "mps"]

							self._test_inference_batch_single_identical(test_max_difference=_UpperCamelCase  )





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




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




				def   __a		(      self  )       ->   Optional[int]:
							lowerCAmelCase_      			=			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"  )

							lowerCAmelCase_      			=			StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"       ,       torch_dtype=torch.floataa  )
							pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase_      			=			torch.Generator(device="cpu"  ).manual_seed(0  )
							lowerCAmelCase_      			=			pipe("anime turle"       ,       generator=_UpperCamelCase       ,       output_type="np"  )

							lowerCAmelCase_      			=			output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(_UpperCamelCase       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   Optional[Any]:
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowerCAmelCase_      			=			StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"       ,       torch_dtype=torch.floataa  )
							lowerCAmelCase_      			=			pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase_      			=			pipe(
							    "anime turtle"       ,       prior_num_inference_steps=2       ,       num_inference_steps=2       ,       output_type="np"       ,       )

							lowerCAmelCase_      			=			torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9



