


'''simple docstring'''

import os
import re
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


lowerCAmelCase				:Union[str, Any]        =      logging.get_logger(__name__)

lowerCAmelCase				:Optional[int]        =      {'''vocab_file''': '''spiece.model'''}

lowerCAmelCase				:List[str]        =      {
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

lowerCAmelCase				:Tuple        =      {
    '''google/bigbird-roberta-base''': 4_0_9_6,
    '''google/bigbird-roberta-large''': 4_0_9_6,
    '''google/bigbird-base-trivia-itc''': 4_0_9_6,
}
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      str				   =    VOCAB_FILES_NAMES
		A_     :      int				   =    PRETRAINED_VOCAB_FILES_MAP
		A_     :      int				   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_     :      List[Any]				   =    ["""input_ids""", """attention_mask"""]
		A_     :      List[int]				   =    []







		def __init__( self		:						Any  ,					_A		:						Optional[int]  ,					_A		:						Dict="<unk>"  ,					_A		:						List[str]="<s>"  ,					_A		:						Optional[int]="</s>"  ,					_A		:						Union[str, Any]="<pad>"  ,					_A		:						int="[SEP]"  ,					_A		:						Optional[Any]="[MASK]"  ,					_A		:						Tuple="[CLS]"  ,					_A		:						Optional[Dict[str, Any]] = None  ,					**_A		:						Optional[int]  ,					)  ->			None:
			__magic_name__	:   Union[str, Any]					     =					AddedToken(_A  ,					lstrip=_A  ,					rstrip=_A      ) if isinstance(_A  ,					_A      ) else bos_token
			__magic_name__	:   List[str]					     =					AddedToken(_A  ,					lstrip=_A  ,					rstrip=_A      ) if isinstance(_A  ,					_A      ) else eos_token
			__magic_name__	:   List[str]					     =					AddedToken(_A  ,					lstrip=_A  ,					rstrip=_A      ) if isinstance(_A  ,					_A      ) else unk_token
			__magic_name__	:   Union[str, Any]					     =					AddedToken(_A  ,					lstrip=_A  ,					rstrip=_A      ) if isinstance(_A  ,					_A      ) else pad_token
			__magic_name__	:   Optional[Any]					     =					AddedToken(_A  ,					lstrip=_A  ,					rstrip=_A      ) if isinstance(_A  ,					_A      ) else cls_token
			__magic_name__	:   Tuple					     =					AddedToken(_A  ,					lstrip=_A  ,					rstrip=_A      ) if isinstance(_A  ,					_A      ) else sep_token

			# Mask token behave like a normal word, i.e. include the space before it
			__magic_name__	:   Any					     =					AddedToken(_A  ,					lstrip=_A  ,					rstrip=_A      ) if isinstance(_A  ,					_A      ) else mask_token

			__magic_name__	:   int					     =					{} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=_A  ,					eos_token=_A  ,					unk_token=_A  ,					pad_token=_A  ,					sep_token=_A  ,					mask_token=_A  ,					cls_token=_A  ,					sp_model_kwargs=self.sp_model_kwargs  ,					**_A  ,					)

			__magic_name__	:   int					     =					vocab_file

			__magic_name__	:   List[str]					     =					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
			self.sp_model.Load(_A      )







		@property
		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Tuple:
			return self.sp_model.get_piece_size()







		def        __lowerCAmelCase		( self		:						int      )  ->			List[Any]:
			__magic_name__	:   Optional[Any]					     =					{self.convert_ids_to_tokens(_A      ): i for i in range(self.vocab_size      )}
			vocab.update(self.added_tokens_encoder      )
			return vocab







		def __getstate__( self		:						str      )  ->			Dict:
			__magic_name__	:   Optional[int]					     =					self.__dict__.copy()
			__magic_name__	:   List[str]					     =					None
			return state







		def __setstate__( self		:						Optional[int]  ,					_A		:						List[Any]      )  ->			List[Any]:
			__magic_name__	:   Optional[Any]					     =					d

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

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







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						str      )  ->			List[str]:
			return self.sp_model.encode(_A  ,					out_type=_A      )







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						str      )  ->			List[str]:
			return self.sp_model.piece_to_id(_A      )







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						int      )  ->			Optional[Any]:
			__magic_name__	:   Union[str, Any]					     =					self.sp_model.IdToPiece(_A      )
			return token







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						Optional[int]      )  ->			str:
			__magic_name__	:   List[str]					     =					[]
			__magic_name__	:   str					     =					''
			__magic_name__	:   Union[str, Any]					     =					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(_A      ) + token
					__magic_name__	:   Any					     =					True
					__magic_name__	:   Any					     =					[]
				else:
					current_sub_tokens.append(_A      )
					__magic_name__	:   Union[str, Any]					     =					False
			out_string += self.sp_model.decode(_A      )
			return out_string.strip()







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						List[int]  ,					_A		:						bool = False  ,					_A		:						bool = None  ,					_A		:						bool = True  ,					**_A		:						Any  ,					)  ->			str:
			__magic_name__	:   str					     =					kwargs.pop('use_source_tokenizer'  ,					_A      )

			__magic_name__	:   Union[str, Any]					     =					self.convert_ids_to_tokens(_A  ,					skip_special_tokens=_A      )

			# 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
			__magic_name__	:   Optional[int]					     =					[]
			__magic_name__	:   Tuple					     =					[]
			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(_A      )      )
						__magic_name__	:   List[str]					     =					[]
					sub_texts.append(_A      )
				else:
					current_sub_text.append(_A      )
			if current_sub_text:
				sub_texts.append(self.convert_tokens_to_string(_A      )      )

			# Mimic the behavior of the Rust tokenizer:
			# No space before [MASK] and [SEP]
			if spaces_between_special_tokens:
				__magic_name__	:   Any					     =					re.sub(R' (\[(MASK|SEP)\])'  ,					R'\1'  ,					' '.join(_A      )      )
			else:
				__magic_name__	:   Optional[Any]					     =					''.join(_A      )

			__magic_name__	:   List[Any]					     =					(
			    clean_up_tokenization_spaces
			    if clean_up_tokenization_spaces is not None
			    else self.clean_up_tokenization_spaces
			)
			if clean_up_tokenization_spaces:
				__magic_name__	:   int					     =					self.clean_up_tokenization(_A      )
				return clean_text
			else:
				return text







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						str  ,					_A		:						Optional[str] = None      )  ->			Tuple[str]:
			if not os.path.isdir(_A      ):
				logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
				return
			__magic_name__	:   List[str]					     =					os.path.join(
			    _A  ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

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

			return (out_vocab_file,)







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						List[int]  ,					_A		:						Optional[List[int]] = None      )  ->			List[int]:
			if token_ids_a is None:
				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			__magic_name__	:   Union[str, Any]					     =					[self.cls_token_id]
			__magic_name__	:   Optional[int]					     =					[self.sep_token_id]
			return cls + token_ids_a + sep + token_ids_a + sep







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						List[int]  ,					_A		:						Optional[List[int]] = None  ,					_A		:						bool = False      )  ->			List[int]:
			if already_has_special_tokens:
				return super().get_special_tokens_mask(
				    token_ids_a=_A  ,					token_ids_a=_A  ,					already_has_special_tokens=_A      )

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







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						List[int]  ,					_A		:						Optional[List[int]] = None      )  ->			List[int]:
			__magic_name__	:   Any					     =					[self.sep_token_id]
			__magic_name__	:   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]






'''simple docstring'''

import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class _lowerCamelCase	(						lowercase__		,					lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      int				   =    StableDiffusionXLImgaImgPipeline
		A_     :      str				   =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
		A_     :      Optional[Any]				   =    PipelineTesterMixin.required_optional_params - {"""latents"""}
		A_     :      Any				   =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		A_     :      Any				   =    IMAGE_TO_IMAGE_IMAGE_PARAMS
		A_     :      Optional[int]				   =    IMAGE_TO_IMAGE_IMAGE_PARAMS







		def        __lowerCAmelCase		( self		:						Dict      )  ->			Optional[int]:
			torch.manual_seed(0      )
			__magic_name__	:   int					     =					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')  ,					attention_head_dim=(2, 4)  ,					use_linear_projection=_A  ,					addition_embed_type='text_time'  ,					addition_time_embed_dim=8  ,					transformer_layers_per_block=(1, 2)  ,					projection_class_embeddings_input_dim=80  ,					cross_attention_dim=64  ,					)
			__magic_name__	:   str					     =					EulerDiscreteScheduler(
			    beta_start=0.0_0085  ,					beta_end=0.012  ,					steps_offset=1  ,					beta_schedule='scaled_linear'  ,					timestep_spacing='leading'  ,					)
			torch.manual_seed(0      )
			__magic_name__	:   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  ,					sample_size=128  ,					)
			torch.manual_seed(0      )
			__magic_name__	:   Any					     =					CLIPTextConfig(
			    bos_token_id=0  ,					eos_token_id=2  ,					hidden_size=32  ,					intermediate_size=37  ,					layer_norm_eps=1E-05  ,					num_attention_heads=4  ,					num_hidden_layers=5  ,					pad_token_id=1  ,					vocab_size=1000  ,					hidden_act='gelu'  ,					projection_dim=32  ,					)
			__magic_name__	:   Dict					     =					CLIPTextModel(_A      )
			__magic_name__	:   List[str]					     =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  ,					local_files_only=_A      )

			__magic_name__	:   Optional[Any]					     =					CLIPTextModelWithProjection(_A      )
			__magic_name__	:   Dict					     =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  ,					local_files_only=_A      )

			__magic_name__	:   List[Any]					     =					{
			    'unet': unet,
			    'scheduler': scheduler,
			    'vae': vae,
			    'text_encoder': text_encoder,
			    'tokenizer': tokenizer,
			    'text_encoder_2': text_encoder_a,
			    'tokenizer_2': tokenizer_a,
			    # "safety_checker": None,
			    # "feature_extractor": None,
			}
			return components







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						List[str]  ,					_A		:						Any=0      )  ->			Union[str, Any]:
			__magic_name__	:   Any					     =					floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(_A      )      ).to(_A      )
			__magic_name__	:   Dict					     =					image / 2 + 0.5
			if str(_A      ).startswith('mps'      ):
				__magic_name__	:   Any					     =					torch.manual_seed(_A      )
			else:
				__magic_name__	:   int					     =					torch.Generator(device=_A      ).manual_seed(_A      )
			__magic_name__	:   List[Any]					     =					{
			    'prompt': 'A painting of a squirrel eating a burger',
			    'image': image,
			    'generator': generator,
			    'num_inference_steps': 2,
			    'guidance_scale': 5.0,
			    'output_type': 'numpy',
			    'strength': 0.75,
			}
			return inputs







		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			List[Any]:
			__magic_name__	:   str					     =					'cpu'  # ensure determinism for the device-dependent torch.Generator
			__magic_name__	:   str					     =					self.get_dummy_components()
			__magic_name__	:   Any					     =					StableDiffusionXLImgaImgPipeline(**_A      )
			__magic_name__	:   List[Any]					     =					sd_pipe.to(_A      )
			sd_pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Any					     =					self.get_dummy_inputs(_A      )
			__magic_name__	:   Optional[int]					     =					sd_pipe(**_A      ).images
			__magic_name__	:   str					     =					image[0, -3:, -3:, -1]

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

			__magic_name__	:   Any					     =					np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]      )

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







		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Optional[Any]:
			super().test_attention_slicing_forward_pass(expected_max_diff=3E-3      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			int:
			super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







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







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Optional[int]:
			__magic_name__	:   Dict					     =					self.get_dummy_components()
			__magic_name__	:   Optional[Any]					     =					StableDiffusionXLImgaImgPipeline(**_A      )
			__magic_name__	:   List[Any]					     =					sd_pipe.to(_A      )
			__magic_name__	:   str					     =					sd_pipe.to(_A      )
			sd_pipe.set_progress_bar_config(disable=_A      )

			# forward without prompt embeds
			__magic_name__	:   Union[str, Any]					     =					self.get_dummy_inputs(_A      )
			__magic_name__	:   Union[str, Any]					     =					3 * ['this is a negative prompt']
			__magic_name__	:   List[str]					     =					negative_prompt
			__magic_name__	:   int					     =					3 * [inputs['prompt']]

			__magic_name__	:   Tuple					     =					sd_pipe(**_A      )
			__magic_name__	:   str					     =					output.images[0, -3:, -3:, -1]

			# forward with prompt embeds
			__magic_name__	:   Optional[Any]					     =					self.get_dummy_inputs(_A      )
			__magic_name__	:   Tuple					     =					3 * ['this is a negative prompt']
			__magic_name__	:   List[str]					     =					3 * [inputs.pop('prompt'      )]

			(
			    (
			    __magic_name__
			)     ,    (
			    __magic_name__
			)     ,    (
			    __magic_name__
			)     ,    (
			    __magic_name__
			)     ,    
			)	:   List[Any]					     =					sd_pipe.encode_prompt(_A  ,					negative_prompt=_A      )

			__magic_name__	:   Tuple					     =					sd_pipe(
			    **_A  ,					prompt_embeds=_A  ,					negative_prompt_embeds=_A  ,					pooled_prompt_embeds=_A  ,					negative_pooled_prompt_embeds=_A  ,					)
			__magic_name__	:   int					     =					output.images[0, -3:, -3:, -1]

			# make sure that it's equal
			assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()      ).max() < 1E-4







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



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Tuple:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						Optional[int]  ,					_A		:						Optional[Any]="cpu"  ,					_A		:						List[str]=torch.floataa  ,					_A		:						Any=0      )  ->			str:
			__magic_name__	:   List[str]					     =					torch.Generator(device=_A      ).manual_seed(_A      )
			__magic_name__	:   Optional[Any]					     =					np.random.RandomState(_A      ).standard_normal((1, 4, 64, 64)      )
			__magic_name__	:   Union[str, Any]					     =					torch.from_numpy(_A      ).to(device=_A  ,					dtype=_A      )
			__magic_name__	:   Optional[int]					     =					{
			    'prompt': 'a photograph of an astronaut riding a horse',
			    'latents': latents,
			    'generator': generator,
			    'num_inference_steps': 3,
			    'guidance_scale': 7.5,
			    'output_type': 'numpy',
			}
			return inputs







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Dict:
			__magic_name__	:   str					     =					DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Optional[int]					     =					self.get_inputs(_A      )
			__magic_name__	:   Union[str, Any]					     =					pipe(**_A      ).images
			__magic_name__	:   Any					     =					image[0, -3:, -3:, -1].flatten()

			assert image.shape == (1, 512, 512, 3)
			__magic_name__	:   List[Any]					     =					np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]      )
			assert np.abs(image_slice - expected_slice      ).max() < 7E-3