


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import 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


A				:       Union[str, Any]      =  False






@skip_mps
class      UpperCamelCase(	_a  ,				_a  ,				_a  ,				unittest.TestCase ):
					snake_case_		:					int    					=						StableDiffusionAttendAndExcitePipeline
					snake_case_		:					List[str]    					=						False
					snake_case_		:					Union[str, Any]    					=						TEXT_TO_IMAGE_PARAMS
					snake_case_		:					List[str]    					=						TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""} )
					snake_case_		:					Union[str, Any]    					=						TEXT_TO_IMAGE_IMAGE_PARAMS
					snake_case_		:					List[str]    					=						TEXT_TO_IMAGE_IMAGE_PARAMS


					@classmethod
					def 				SCREAMING_SNAKE_CASE_	(    cls						:		Dict    )      ->						int:




											'''simple docstring'''

											super().setUpClass()
											torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE    )


					@classmethod
					def 				SCREAMING_SNAKE_CASE_	(    cls						:		List[str]    )      ->						int:




											'''simple docstring'''

											super().tearDownClass()
											torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						str:




											'''simple docstring'''

											torch.manual_seed(0    )
											__snake_case				=		UNetaDConditionModel(
											    block_out_channels=(3_2, 6_4)  ,     layers_per_block=1  ,     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  ,     attention_head_dim=(2, 4)  ,     use_linear_projection=SCREAMING_SNAKE_CASE  ,     )
											__snake_case				=		DDIMScheduler(
											    beta_start=0.00085  ,     beta_end=0.012  ,     beta_schedule="scaled_linear"  ,     clip_sample=SCREAMING_SNAKE_CASE  ,     set_alpha_to_one=SCREAMING_SNAKE_CASE  ,     )
											torch.manual_seed(0    )
											__snake_case				=		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  ,     sample_size=1_2_8  ,     )
											torch.manual_seed(0    )
											__snake_case				=		CLIPTextConfig(
											    bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=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=1_0_0_0  ,     hidden_act="gelu"  ,     projection_dim=5_1_2  ,     )
											__snake_case				=		CLIPTextModel(SCREAMING_SNAKE_CASE    )
											__snake_case				=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

											__snake_case				=		{
											    "unet": unet,
											    "scheduler": scheduler,
											    "vae": vae,
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "safety_checker": None,
											    "feature_extractor": None,
											}

											return components


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Any  ,     SCREAMING_SNAKE_CASE						:		int=0    )      ->						Tuple:




											'''simple docstring'''

											if str(SCREAMING_SNAKE_CASE    ).startswith("mps"    ):
																	__snake_case				=		torch.manual_seed(SCREAMING_SNAKE_CASE    )
											else:
																	__snake_case				=		torch.Generator(device=SCREAMING_SNAKE_CASE    ).manual_seed(SCREAMING_SNAKE_CASE    )
											__snake_case				=		__snake_case				=		{
											    "prompt": "a cat and a frog",
											    "token_indices": [2, 5],
											    "generator": generator,
											    "num_inference_steps": 1,
											    "guidance_scale": 6.0,
											    "output_type": "numpy",
											    "max_iter_to_alter": 2,
											    "thresholds": {0: 0.7},
											}
											return inputs


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




											'''simple docstring'''

											__snake_case				=		"cpu"

											__snake_case				=		self.get_dummy_components()
											__snake_case				=		self.pipeline_class(**SCREAMING_SNAKE_CASE    )
											pipe.to(SCREAMING_SNAKE_CASE    )
											pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE    )

											__snake_case				=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE    )
											__snake_case				=		pipe(**SCREAMING_SNAKE_CASE    ).images
											__snake_case				=		image[0, -3:, -3:, -1]

											self.assertEqual(image.shape  ,     (1, 6_4, 6_4, 3)    )
											__snake_case				=		np.array(
											    [0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496]    )
											__snake_case				=		np.abs(image_slice.flatten() - expected_slice    ).max()
											self.assertLessEqual(SCREAMING_SNAKE_CASE  ,     1e-3    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						List[Any]:




											'''simple docstring'''

											super().test_cpu_offload_forward_pass(expected_max_diff=5e-4    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						int:




											'''simple docstring'''

											self._test_inference_batch_consistent(batch_sizes=[1, 2]    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Dict:




											'''simple docstring'''

											self._test_inference_batch_single_identical(batch_size=2  ,     expected_max_diff=7e-4    )


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




											'''simple docstring'''

											super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )


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




											'''simple docstring'''

											super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4    )


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




											'''simple docstring'''

											super().test_save_load_local(expected_max_difference=5e-4    )






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




											'''simple docstring'''

											super().test_save_load_optional_components(expected_max_difference=4e-4    )






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


					@classmethod
					def 				SCREAMING_SNAKE_CASE_	(    cls						:		Tuple    )      ->						int:




											'''simple docstring'''

											super().setUpClass()
											torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE    )


					@classmethod
					def 				SCREAMING_SNAKE_CASE_	(    cls						:		int    )      ->						List[Any]:




											'''simple docstring'''

											super().tearDownClass()
											torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						str:




											'''simple docstring'''

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






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




											'''simple docstring'''

											__snake_case				=		torch.manual_seed(5_1    )

											__snake_case				=		StableDiffusionAttendAndExcitePipeline.from_pretrained(
											    "CompVis/stable-diffusion-v1-4"  ,     safety_checker=SCREAMING_SNAKE_CASE  ,     torch_dtype=torch.floataa    )
											pipe.to("cuda"    )

											__snake_case				=		"a painting of an elephant with glasses"
											__snake_case				=		[5, 7]

											__snake_case				=		pipe(
											    prompt=SCREAMING_SNAKE_CASE  ,     token_indices=SCREAMING_SNAKE_CASE  ,     guidance_scale=7.5  ,     generator=SCREAMING_SNAKE_CASE  ,     num_inference_steps=5  ,     max_iter_to_alter=5  ,     output_type="numpy"  ,     ).images[0]

											__snake_case				=		load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"    )
											assert np.abs((expected_image - image).max()    ) < 5e-1









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 SPIECE_UNDERLINE, logging


A				:       List[str]      =  logging.get_logger(__name__)

A				:       List[Any]      =  {'vocab_file': 'spiece.model'}

A				:       Tuple      =  {
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

A				:       Any      =  {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
A				:       Tuple      =  0
A				:       str      =  1
A				:       str      =  2
A				:       Union[str, Any]      =  3
A				:       Optional[Any]      =  4






class      UpperCamelCase(	_a ):
					snake_case_		:					Union[str, Any]    					=						VOCAB_FILES_NAMES
					snake_case_		:					List[Any]    					=						PRETRAINED_VOCAB_FILES_MAP
					snake_case_		:					str    					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					snake_case_		:					Tuple    					=						"""left"""


					def __init__(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		Any=False  ,     SCREAMING_SNAKE_CASE						:		List[Any]=True  ,     SCREAMING_SNAKE_CASE						:		Dict=False  ,     SCREAMING_SNAKE_CASE						:		Tuple="<s>"  ,     SCREAMING_SNAKE_CASE						:		Optional[int]="</s>"  ,     SCREAMING_SNAKE_CASE						:		List[str]="<unk>"  ,     SCREAMING_SNAKE_CASE						:		List[str]="<sep>"  ,     SCREAMING_SNAKE_CASE						:		Optional[int]="<pad>"  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]="<cls>"  ,     SCREAMING_SNAKE_CASE						:		str="<mask>"  ,     SCREAMING_SNAKE_CASE						:		int=["<eop>", "<eod>"]  ,     SCREAMING_SNAKE_CASE						:		Optional[Dict[str, Any]] = None  ,     **SCREAMING_SNAKE_CASE						:		Dict  ,     )      ->						None:




											'''simple docstring'''

											__snake_case				=		AddedToken(SCREAMING_SNAKE_CASE  ,     lstrip=SCREAMING_SNAKE_CASE  ,     rstrip=SCREAMING_SNAKE_CASE    ) if isinstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ) else mask_token

											__snake_case				=		{} if sp_model_kwargs is None else sp_model_kwargs

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

											__snake_case				=		3

											__snake_case				=		do_lower_case
											__snake_case				=		remove_space
											__snake_case				=		keep_accents
											__snake_case				=		vocab_file

											__snake_case				=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
											self.sp_model.Load(SCREAMING_SNAKE_CASE    )


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




											'''simple docstring'''

											return len(self.sp_model    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		{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						:		Any    )      ->						str:




											'''simple docstring'''

											__snake_case				=		self.__dict__.copy()
											__snake_case				=		None
											return state


					def __setstate__(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[int]    )      ->						int:




											'''simple docstring'''

											__snake_case				=		d

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

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		List[Any]    )      ->						int:




											'''simple docstring'''

											if self.remove_space:
																	__snake_case				=		" ".join(inputs.strip().split()    )
											else:
																	__snake_case				=		inputs
											__snake_case				=		outputs.replace("``"  ,     "\""    ).replace("''"  ,     "\""    )

											if not self.keep_accents:
																	__snake_case				=		unicodedata.normalize("NFKD"  ,     SCREAMING_SNAKE_CASE    )
																	__snake_case				=		"".join([c for c in outputs if not unicodedata.combining(SCREAMING_SNAKE_CASE    )]    )
											if self.do_lower_case:
																	__snake_case				=		outputs.lower()

											return outputs


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		self.preprocess_text(SCREAMING_SNAKE_CASE    )
											__snake_case				=		self.sp_model.encode(SCREAMING_SNAKE_CASE  ,     out_type=SCREAMING_SNAKE_CASE    )
											__snake_case				=		[]
											for piece in pieces:
																	if len(SCREAMING_SNAKE_CASE    ) > 1 and piece[-1] == str(","    ) and piece[-2].isdigit():
																							__snake_case				=		self.sp_model.EncodeAsPieces(piece[:-1].replace(SCREAMING_SNAKE_CASE  ,     ""    )    )
																							if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																													if len(cur_pieces[0]    ) == 1:
																																			__snake_case				=		cur_pieces[1:]
																													else:
																																			__snake_case				=		cur_pieces[0][1:]
																							cur_pieces.append(piece[-1]    )
																							new_pieces.extend(SCREAMING_SNAKE_CASE    )
																	else:
																							new_pieces.append(SCREAMING_SNAKE_CASE    )

											return new_pieces


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Optional[int]    )      ->						Optional[Any]:




											'''simple docstring'''

											return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		int    )      ->						List[Any]:




											'''simple docstring'''

											__snake_case				=		"".join(SCREAMING_SNAKE_CASE    ).replace(SCREAMING_SNAKE_CASE  ,     " "    ).strip()
											return out_string


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		List[int]  ,     SCREAMING_SNAKE_CASE						:		bool = False  ,     SCREAMING_SNAKE_CASE						:		bool = None  ,     SCREAMING_SNAKE_CASE						:		bool = True  ,     **SCREAMING_SNAKE_CASE						:		Optional[int]  ,     )      ->						str:




											'''simple docstring'''

											__snake_case				=		kwargs.pop("use_source_tokenizer"  ,     SCREAMING_SNAKE_CASE    )

											__snake_case				=		self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE  ,     skip_special_tokens=SCREAMING_SNAKE_CASE    )

											# To avoid mixing byte-level and unicode for byte-level BPT
											# we need to build string separately for added tokens and byte-level tokens
											# cf. https://github.com/huggingface/transformers/issues/1133
											__snake_case				=		[]
											__snake_case				=		[]
											for token in filtered_tokens:
																	if skip_special_tokens and token in self.all_special_ids:
																							continue
																	if token in self.added_tokens_encoder:
																							if current_sub_text:
																													sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE    )    )
																													__snake_case				=		[]
																							sub_texts.append(SCREAMING_SNAKE_CASE    )
																	else:
																							current_sub_text.append(SCREAMING_SNAKE_CASE    )
											if current_sub_text:
																	sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE    )    )

											# Mimic the behavior of the Rust tokenizer:
											# By default, there are no spaces between special tokens
											__snake_case				=		"".join(SCREAMING_SNAKE_CASE    )

											__snake_case				=		(
											    clean_up_tokenization_spaces
											    if clean_up_tokenization_spaces is not None
											    else self.clean_up_tokenization_spaces
											)
											if clean_up_tokenization_spaces:
																	__snake_case				=		self.clean_up_tokenization(SCREAMING_SNAKE_CASE    )
																	return clean_text
											else:
																	return text


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




											'''simple docstring'''

											__snake_case				=		[self.sep_token_id]
											__snake_case				=		[self.cls_token_id]
											if token_ids_a is None:
																	return token_ids_a + sep + cls
											return token_ids_a + sep + token_ids_a + sep + cls


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		List[int]  ,     SCREAMING_SNAKE_CASE						:		Optional[List[int]] = None  ,     SCREAMING_SNAKE_CASE						:		bool = False    )      ->						List[int]:




											'''simple docstring'''

											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 not None:
																	return ([0] * len(SCREAMING_SNAKE_CASE    )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE    )) + [1, 1]
											return ([0] * len(SCREAMING_SNAKE_CASE    )) + [1, 1]


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




											'''simple docstring'''

											__snake_case				=		[self.sep_token_id]
											__snake_case				=		[2]

											if token_ids_a is None:
																	return len(token_ids_a + sep    ) * [0] + cls_segment_id
											return len(token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1] + cls_segment_id






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Optional[str] = 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
											__snake_case				=		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:
																							__snake_case				=		self.sp_model.serialized_model_proto()
																							fi.write(SCREAMING_SNAKE_CASE    )

											return (out_vocab_file,)





