





"""simple docstring"""




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  __UpperCamelCase       (		a__	, a__	, a__	, unittest.TestCase		):
							lowerCamelCase     :      str         =StableUnCLIPPipeline
							lowerCamelCase     :      Optional[Any]         =TEXT_TO_IMAGE_PARAMS
							lowerCamelCase     :      List[str]         =TEXT_TO_IMAGE_BATCH_PARAMS
							lowerCamelCase     :      str         =TEXT_TO_IMAGE_IMAGE_PARAMS
							lowerCamelCase     :      int         =TEXT_TO_IMAGE_IMAGE_PARAMS

							# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
							lowerCamelCase     :      Union[str, Any]         =False






							def 					__a     (     self   )			->				Any:
								a   :	Optional[int]			=       32
								a   :	str			=       embedder_hidden_size

								# prior components

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

								torch.manual_seed(0   )
								a   :	List[Any]			=       CLIPTextModelWithProjection(
								    CLIPTextConfig(
								        bos_token_id=0   ,      eos_token_id=2   ,      hidden_size=lowerCAmelCase__   ,      projection_dim=lowerCAmelCase__   ,      intermediate_size=37   ,      layer_norm_eps=1E-05   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      pad_token_id=1   ,      vocab_size=1000   ,      )   )

								torch.manual_seed(0   )
								a   :	Any			=       PriorTransformer(
								    num_attention_heads=2   ,      attention_head_dim=12   ,      embedding_dim=lowerCAmelCase__   ,      num_layers=1   ,      )

								torch.manual_seed(0   )
								a   :	Dict			=       DDPMScheduler(
								    variance_type="fixed_small_log"   ,      prediction_type="sample"   ,      num_train_timesteps=1000   ,      clip_sample=lowerCAmelCase__   ,      clip_sample_range=5.0   ,      beta_schedule="squaredcos_cap_v2"   ,      )

								# regular denoising components

								torch.manual_seed(0   )
								a   :	str			=       StableUnCLIPImageNormalizer(embedding_dim=lowerCAmelCase__   )
								a   :	Optional[int]			=       DDPMScheduler(beta_schedule="squaredcos_cap_v2"   )

								torch.manual_seed(0   )
								a   :	List[Any]			=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

								torch.manual_seed(0   )
								a   :	Optional[int]			=       CLIPTextModel(
								    CLIPTextConfig(
								        bos_token_id=0   ,      eos_token_id=2   ,      hidden_size=lowerCAmelCase__   ,      projection_dim=32   ,      intermediate_size=37   ,      layer_norm_eps=1E-05   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      pad_token_id=1   ,      vocab_size=1000   ,      )   )

								torch.manual_seed(0   )
								a   :	Dict			=       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=lowerCAmelCase__   ,      layers_per_block=1   ,      upcast_attention=lowerCAmelCase__   ,      use_linear_projection=lowerCAmelCase__   ,      )

								torch.manual_seed(0   )
								a   :	List[Any]			=       DDIMScheduler(
								    beta_schedule="scaled_linear"   ,      beta_start=0.00_085   ,      beta_end=0.012   ,      prediction_type="v_prediction"   ,      set_alpha_to_one=lowerCAmelCase__   ,      steps_offset=1   ,      )

								torch.manual_seed(0   )
								a   :	Union[str, Any]			=       AutoencoderKL()

								a   :	int			=       {
								    # 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   ,      lowerCAmelCase__   ,      lowerCAmelCase__=0   )			->				Any:
								if str(lowerCAmelCase__   ).startswith("mps"   ):
									a   :	Optional[Any]			=       torch.manual_seed(lowerCAmelCase__   )
								else:
									a   :	int			=       torch.Generator(device=lowerCAmelCase__   ).manual_seed(lowerCAmelCase__   )
								a   :	Any			=       {
								    "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   )			->				Union[str, Any]:
								a   :	Optional[int]			=       torch_device == "cpu"

								self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase__   )






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

								self._test_inference_batch_single_identical(test_max_difference=lowerCAmelCase__   )


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






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






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

								a   :	Any			=       StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"   ,      torch_dtype=torch.floataa   )
								pipe.to(lowerCAmelCase__   )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__   )
								# 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()

								a   :	List[str]			=       torch.Generator(device="cpu"   ).manual_seed(0   )
								a   :	Tuple			=       pipe("anime turle"   ,      generator=lowerCAmelCase__   ,      output_type="np"   )

								a   :	Tuple			=       output.images[0]

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

								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )






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

								a   :	List[Any]			=       StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"   ,      torch_dtype=torch.floataa   )
								a   :	Union[str, Any]			=       pipe.to(lowerCAmelCase__   )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__   )
								pipe.enable_attention_slicing()
								pipe.enable_sequential_cpu_offload()

								a   :	Tuple			=       pipe(
								    "anime turtle"   ,      prior_num_inference_steps=2   ,      num_inference_steps=2   ,      output_type="np"   ,      )

								a   :	Optional[Any]			=       torch.cuda.max_memory_allocated()
								# make sure that less than 7 GB is allocated
								assert mem_bytes < 7 * 10**9









"""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_albert import AlbertTokenizer
else:
		a		:				Tuple  			=    None

a		:				Any  			=    logging.get_logger(__name__)
a		:				List[Any]  			=    {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:				str  			=    {
    '''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''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

a		:				str  			=    {
    '''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,
}

a		:				Union[str, Any]  			=    '''▁'''


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Union[str, Any]         =VOCAB_FILES_NAMES
							lowerCamelCase     :      Dict         =PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase     :      List[Any]         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase     :      List[Any]         =AlbertTokenizer






							def __init__(     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__="[CLS]"   ,      lowerCAmelCase__="[SEP]"   ,      lowerCAmelCase__="<unk>"   ,      lowerCAmelCase__="[SEP]"   ,      lowerCAmelCase__="<pad>"   ,      lowerCAmelCase__="[CLS]"   ,      lowerCAmelCase__="[MASK]"   ,      **lowerCAmelCase__   ,      )			->				Union[str, Any]:
								# Mask token behave like a normal word, i.e. include the space before it and
								# is included in the raw text, there should be a match in a non-normalized sentence.
								a   :	Optional[int]			=       (
								    AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ,      normalized=lowerCAmelCase__   )
								    if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   )
								    else mask_token
								)

								super().__init__(
								    lowerCAmelCase__   ,      tokenizer_file=lowerCAmelCase__   ,      do_lower_case=lowerCAmelCase__   ,      remove_space=lowerCAmelCase__   ,      keep_accents=lowerCAmelCase__   ,      bos_token=lowerCAmelCase__   ,      eos_token=lowerCAmelCase__   ,      unk_token=lowerCAmelCase__   ,      sep_token=lowerCAmelCase__   ,      pad_token=lowerCAmelCase__   ,      cls_token=lowerCAmelCase__   ,      mask_token=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )

								a   :	Dict			=       do_lower_case
								a   :	Any			=       remove_space
								a   :	Optional[Any]			=       keep_accents
								a   :	List[str]			=       vocab_file
								a   :	Optional[Any]			=       False if not self.vocab_file else True






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Optional[Any]			=       [self.sep_token_id]
								a   :	int			=       [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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Optional[Any]			=       [self.sep_token_id]
								a   :	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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[str]:
								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(lowerCAmelCase__   ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
									return
								a   :	Dict			=       os.path.join(
								    lowerCAmelCase__   ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

								if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCAmelCase__   ):
									copyfile(self.vocab_file   ,      lowerCAmelCase__   )

								return (out_vocab_file,)


