


"""simple docstring"""


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_camembert import CamembertTokenizer
else:
    __lowerCamelCase      :Any	    =		None


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

__lowerCamelCase      :str	    =		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase      :List[str]	    =		{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

__lowerCamelCase      :List[str]	    =		{
    'camembert-base': 512,
}

__lowerCamelCase      :Tuple	    =		'▁'


class 			A__	(						__SCREAMING_SNAKE_CASE):


     """simple docstring"""
     snake_case__	:					int													=VOCAB_FILES_NAMES
     snake_case__	:					Union[str, Any]													=PRETRAINED_VOCAB_FILES_MAP
     snake_case__	:					Optional[Any]													=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case__	:					Dict													=['input_ids', 'attention_mask']
     snake_case__	:					int													=CamembertTokenizer

     def __init__(	self:						List[str]     ,		__a:						Optional[Any]=None     ,		__a:						Any=None     ,		__a:						Optional[Any]="<s>"     ,		__a:						Tuple="</s>"     ,		__a:						int="</s>"     ,		__a:						Union[str, Any]="<s>"     ,		__a:						Tuple="<unk>"     ,		__a:						Union[str, Any]="<pad>"     ,		__a:						List[Any]="<mask>"     ,		__a:						Tuple=["<s>NOTUSED", "</s>NOTUSED"]     ,		**__a:						str     ,		)->    Optional[int]:
           # Mask token behave like a normal word, i.e. include the space before it
           lowerCamelCase					:					Dict        =	AddedToken(_a     ,		lstrip=_a     ,		rstrip=_a	) if isinstance(_a     ,		_a	) else mask_token

           super().__init__(
               _a     ,		tokenizer_file=_a     ,		bos_token=_a     ,		eos_token=_a     ,		sep_token=_a     ,		cls_token=_a     ,		unk_token=_a     ,		pad_token=_a     ,		mask_token=_a     ,		additional_special_tokens=_a     ,		**_a     ,		)

           lowerCamelCase					:					int        =	vocab_file
           lowerCamelCase					:					int        =	False if not self.vocab_file else True

     def 		a__						(	self:						Optional[int]     ,		__a:						Tuple     ,		__a:						Tuple = None	)->    int:

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

     def 		a__						(	self:						Tuple     ,		__a:						int     ,		__a:						str = None	)->    int:
           lowerCamelCase					:					Tuple        =	[self.sep_token_id]
           lowerCamelCase					:					Tuple        =	[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 + sep + token_ids_a + sep	) * [0]







     def 		a__						(	self:						Union[str, Any]     ,		__a:						str     ,		__a:						List[str] = None	)->    Union[str, Any]:
           if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                     """tokenizer."""	)

           if not os.path.isdir(_a	):
                 logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
                 return
           lowerCamelCase					:					Tuple        =	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	):
                 copyfile(self.vocab_file     ,		_a	)

           return (out_vocab_file,)









"""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 			A__	(						__lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					str													=StableDiffusionXLImgaImgPipeline
     snake_case__	:					Any													=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
     snake_case__	:					Optional[int]													=PipelineTesterMixin.required_optional_params - {'''latents'''}
     snake_case__	:					Dict													=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     snake_case__	:					Tuple													=IMAGE_TO_IMAGE_IMAGE_PARAMS
     snake_case__	:					List[str]													=IMAGE_TO_IMAGE_IMAGE_PARAMS

     def 		a__						(	self:						List[str]	)->    int:
           torch.manual_seed(0	)
           lowerCamelCase					:					Any        =	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     ,		)
           lowerCamelCase					:					Any        =	EulerDiscreteScheduler(
               beta_start=0.0_00_85     ,		beta_end=0.0_12     ,		steps_offset=1     ,		beta_schedule="""scaled_linear"""     ,		timestep_spacing="""leading"""     ,		)
           torch.manual_seed(0	)
           lowerCamelCase					:					Any        =	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	)
           lowerCamelCase					:					Optional[Any]        =	CLIPTextConfig(
               bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=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     ,		hidden_act="""gelu"""     ,		projection_dim=32     ,		)
           lowerCamelCase					:					Dict        =	CLIPTextModel(__a	)
           lowerCamelCase					:					Union[str, Any]        =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     ,		local_files_only=__a	)

           lowerCamelCase					:					Dict        =	CLIPTextModelWithProjection(__a	)
           lowerCamelCase					:					Optional[int]        =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     ,		local_files_only=__a	)

           lowerCamelCase					:					str        =	{
               """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 		a__						(	self:						Any     ,		__a:						str     ,		__a:						Tuple=0	)->    Union[str, Any]:
           lowerCamelCase					:					List[Any]        =	floats_tensor((1, 3, 32, 32)     ,		rng=random.Random(__a	)	).to(__a	)
           lowerCamelCase					:					Any        =	image / 2 + 0.5
           if str(__a	).startswith("""mps"""	):
                 lowerCamelCase					:					Dict        =	torch.manual_seed(__a	)
           else:
                 lowerCamelCase					:					Tuple        =	torch.Generator(device=__a	).manual_seed(__a	)
           lowerCamelCase					:					Tuple        =	{
               """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 		a__						(	self:						Dict	)->    Optional[Any]:
           lowerCamelCase					:					Any        =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase					:					Union[str, Any]        =	self.get_dummy_components()
           lowerCamelCase					:					Optional[int]        =	StableDiffusionXLImgaImgPipeline(**__a	)
           lowerCamelCase					:					int        =	sd_pipe.to(__a	)
           sd_pipe.set_progress_bar_config(disable=__a	)

           lowerCamelCase					:					Optional[Any]        =	self.get_dummy_inputs(__a	)
           lowerCamelCase					:					Optional[int]        =	sd_pipe(**__a	).images
           lowerCamelCase					:					Tuple        =	image[0, -3:, -3:, -1]

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

           lowerCamelCase					:					Any        =	np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]	)

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

     def 		a__						(	self:						Optional[int]	)->    Union[str, Any]:
           super().test_attention_slicing_forward_pass(expected_max_diff=3e-3	)

     def 		a__						(	self:						Optional[Any]	)->    str:
           super().test_inference_batch_single_identical(expected_max_diff=3e-3	)

     def 		a__						(	self:						List[str]	)->    Optional[Any]:
           pass







     def 		a__						(	self:						List[Any]	)->    Union[str, Any]:
           lowerCamelCase					:					Tuple        =	self.get_dummy_components()
           lowerCamelCase					:					Union[str, Any]        =	StableDiffusionXLImgaImgPipeline(**__a	)
           lowerCamelCase					:					str        =	sd_pipe.to(__a	)
           lowerCamelCase					:					Any        =	sd_pipe.to(__a	)
           sd_pipe.set_progress_bar_config(disable=__a	)

           # forward without prompt embeds
           lowerCamelCase					:					Dict        =	self.get_dummy_inputs(__a	)
           lowerCamelCase					:					Any        =	3 * ["""this is a negative prompt"""]
           lowerCamelCase					:					Optional[int]        =	negative_prompt
           lowerCamelCase					:					Tuple        =	3 * [inputs["""prompt"""]]

           lowerCamelCase					:					List[Any]        =	sd_pipe(**__a	)
           lowerCamelCase					:					Optional[int]        =	output.images[0, -3:, -3:, -1]

           # forward with prompt embeds
           lowerCamelCase					:					Tuple        =	self.get_dummy_inputs(__a	)
           lowerCamelCase					:					List[Any]        =	3 * ["""this is a negative prompt"""]
           lowerCamelCase					:					Tuple        =	3 * [inputs.pop("""prompt"""	)]

           (
               (
               lowerCamelCase
           )     ,			(
               lowerCamelCase
           )     ,			(
               lowerCamelCase
           )     ,			(
               lowerCamelCase
           )     ,			
           )					:					Union[str, Any]        =	sd_pipe.encode_prompt(__a     ,		negative_prompt=__a	)

           lowerCamelCase					:					int        =	sd_pipe(
               **__a     ,		prompt_embeds=__a     ,		negative_prompt_embeds=__a     ,		pooled_prompt_embeds=__a     ,		negative_pooled_prompt_embeds=__a     ,		)
           lowerCamelCase					:					Union[str, Any]        =	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 			A__	(						unittest.TestCase):


     """simple docstring"""

     def 		a__						(	self:						Dict	)->    str:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()

     def 		a__						(	self:						Union[str, Any]     ,		__a:						Any     ,		__a:						Any="cpu"     ,		__a:						str=torch.floataa     ,		__a:						Any=0	)->    Optional[Any]:
           lowerCamelCase					:					Optional[Any]        =	torch.Generator(device=__a	).manual_seed(__a	)
           lowerCamelCase					:					List[Any]        =	np.random.RandomState(__a	).standard_normal((1, 4, 64, 64)	)
           lowerCamelCase					:					List[str]        =	torch.from_numpy(__a	).to(device=__a     ,		dtype=__a	)
           lowerCamelCase					:					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 		a__						(	self:						Optional[int]	)->    List[str]:
           lowerCamelCase					:					Tuple        =	DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""	)
           pipe.to(__a	)
           pipe.set_progress_bar_config(disable=__a	)

           lowerCamelCase					:					Optional[int]        =	self.get_inputs(__a	)
           lowerCamelCase					:					Optional[Any]        =	pipe(**__a	).images
           lowerCamelCase					:					Dict        =	image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)
           lowerCamelCase					:					List[str]        =	np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]	)
           assert np.abs(image_slice - expected_slice	).max() < 7e-3




