


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       __magic_name__	(     __snake_case					,				__snake_case					,				__snake_case					,				unittest.TestCase):
					A:		Dict			=      StableUnCLIPPipeline
					A:		List[str]			=      TEXT_TO_IMAGE_PARAMS
					A:		Tuple			=      TEXT_TO_IMAGE_BATCH_PARAMS
					A:		Optional[Any]			=      TEXT_TO_IMAGE_IMAGE_PARAMS
					A:		List[Any]			=      TEXT_TO_IMAGE_IMAGE_PARAMS

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


					def      UpperCAmelCase__	(      self							:     str			)  ->      int:





						'''simple docstring'''






						UpperCamelCase__					:    List[Any]          =   32
						UpperCamelCase__					:    Union[str, Any]          =   embedder_hidden_size

						# prior components

						torch.manual_seed(0			)
						UpperCamelCase__					:    Tuple          =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

						torch.manual_seed(0			)
						UpperCamelCase__					:    Any          =   CLIPTextModelWithProjection(
						    CLIPTextConfig(
						        bos_token_id=0							,	eos_token_id=2							,	hidden_size=A_							,	projection_dim=A_							,	intermediate_size=37							,	layer_norm_eps=1E-0_5							,	num_attention_heads=4							,	num_hidden_layers=5							,	pad_token_id=1							,	vocab_size=1000							,	)			)

						torch.manual_seed(0			)
						UpperCamelCase__					:    Optional[int]          =   PriorTransformer(
						    num_attention_heads=2							,	attention_head_dim=12							,	embedding_dim=A_							,	num_layers=1							,	)

						torch.manual_seed(0			)
						UpperCamelCase__					:    Optional[Any]          =   DDPMScheduler(
						    variance_type='''fixed_small_log'''							,	prediction_type='''sample'''							,	num_train_timesteps=1000							,	clip_sample=A_							,	clip_sample_range=5.0							,	beta_schedule='''squaredcos_cap_v2'''							,	)

						# regular denoising components

						torch.manual_seed(0			)
						UpperCamelCase__					:    Tuple          =   StableUnCLIPImageNormalizer(embedding_dim=A_			)
						UpperCamelCase__					:    Optional[int]          =   DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''			)

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

						torch.manual_seed(0			)
						UpperCamelCase__					:    Dict          =   CLIPTextModel(
						    CLIPTextConfig(
						        bos_token_id=0							,	eos_token_id=2							,	hidden_size=A_							,	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=1000							,	)			)

						torch.manual_seed(0			)
						UpperCamelCase__					:    Optional[Any]          =   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=A_							,	layers_per_block=1							,	upcast_attention=A_							,	use_linear_projection=A_							,	)

						torch.manual_seed(0			)
						UpperCamelCase__					:    Union[str, Any]          =   DDIMScheduler(
						    beta_schedule='''scaled_linear'''							,	beta_start=0.0_0085							,	beta_end=0.012							,	prediction_type='''v_prediction'''							,	set_alpha_to_one=A_							,	steps_offset=1							,	)

						torch.manual_seed(0			)
						UpperCamelCase__					:    Any          =   AutoencoderKL()

						UpperCamelCase__					:    List[Any]          =   {
						    # 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      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     Tuple=0			)  ->      List[Any]:





						'''simple docstring'''






						if str(A_			).startswith('''mps'''			):
							UpperCamelCase__					:    List[str]          =   torch.manual_seed(A_			)
						else:
							UpperCamelCase__					:    str          =   torch.Generator(device=A_			).manual_seed(A_			)
						UpperCamelCase__					:    int          =   {
						    "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      UpperCAmelCase__	(      self							:     Tuple			)  ->      Union[str, Any]:





						'''simple docstring'''






						UpperCamelCase__					:    Any          =   torch_device == "cpu"

						self._test_attention_slicing_forward_pass(test_max_difference=A_			)


					def      UpperCAmelCase__	(      self							:     str			)  ->      Any:





						'''simple docstring'''






						UpperCamelCase__					:    List[str]          =   torch_device in ["cpu", "mps"]

						self._test_inference_batch_single_identical(test_max_difference=A_			)



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


					def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      str:





						'''simple docstring'''






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


					def      UpperCAmelCase__	(      self							:     str			)  ->      str:





						'''simple docstring'''






						UpperCamelCase__					:    Optional[int]          =   load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''			)

						UpperCamelCase__					:    Union[str, Any]          =   StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''							,	torch_dtype=torch.floataa			)
						pipe.to(A_			)
						pipe.set_progress_bar_config(disable=A_			)
						# 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()

						UpperCamelCase__					:    Optional[int]          =   torch.Generator(device='''cpu'''			).manual_seed(0			)
						UpperCamelCase__					:    Tuple          =   pipe('''anime turle'''							,	generator=A_							,	output_type='''np'''			)

						UpperCamelCase__					:    str          =   output.images[0]

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

						assert_mean_pixel_difference(A_							,	A_			)


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





						'''simple docstring'''






						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

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

						UpperCamelCase__					:    List[Any]          =   pipe(
						    '''anime turtle'''							,	prior_num_inference_steps=2							,	num_inference_steps=2							,	output_type='''np'''							,	)

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






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:
       __UpperCamelCase       :       Tuple						       =				None


__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Any						       =				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase       :       Tuple						       =				{
    "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",
    },
}

__UpperCamelCase       :       Optional[Any]						       =				{
    "camembert-base": 512,
}

__UpperCamelCase       :       Optional[Any]						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		int			=      VOCAB_FILES_NAMES
     A:		Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		Optional[int]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		List[str]			=      ["input_ids", "attention_mask"]
     A:		Dict			=      CamembertTokenizer


     def __init__(      self							:     int							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Dict="<s>"							,	lowerCamelCase__							:     List[str]="</s>"							,	lowerCamelCase__							:     int="</s>"							,	lowerCamelCase__							:     List[str]="<s>"							,	lowerCamelCase__							:     Optional[int]="<unk>"							,	lowerCamelCase__							:     Union[str, Any]="<pad>"							,	lowerCamelCase__							:     List[Any]="<mask>"							,	lowerCamelCase__							:     List[Any]=["<s>NOTUSED", "</s>NOTUSED"]							,	**lowerCamelCase__							:     Optional[int]							,	)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   AddedToken(lowerCamelCase__							,	lstrip=lowerCamelCase__							,	rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else mask_token

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

      UpperCamelCase__					:    Tuple          =   vocab_file
      UpperCamelCase__					:    Optional[Any]          =   False if not self.vocab_file else True


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      UpperCamelCase__					:    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      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






      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
      UpperCamelCase__					:    List[str]          =   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,)


