





import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class   __lowerCAmelCase			(					_UpperCamelCase		,unittest.TestCase  ):
   _UpperCamelCase :						int     		=				LxmertTokenizer
   _UpperCamelCase :						Union[str, Any]     		=				LxmertTokenizerFast
   _UpperCamelCase :						Tuple     		=				True
   _UpperCamelCase :						str     		=				True





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      super().setUp()

      a__	:						Optional[int]      		=  [
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      a__	:						Dict      		=  os.path.join(self.tmpdirname					,			VOCAB_FILES_NAMES["vocab_file"]      )
      with open(self.vocab_file					,			"w"					,			encoding="utf-8"      ) as vocab_writer:
         vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )





   def 	_snake_case ( self					,			snake_case      )				->  Union[str, Any]:
      """simple docstring"""





      a__	:						Any      		=  "UNwant\u00E9d,running"
      a__	:						Union[str, Any]      		=  "unwanted, running"
      return input_text, output_text





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__	:						Dict      		=  self.tokenizer_class(self.vocab_file      )

      a__	:						Optional[Any]      		=  tokenizer.tokenize("UNwant\u00E9d,running"      )
      self.assertListEqual(_UpperCAmelCase					,			["un", "##want", "##ed", ",", "runn", "##ing"]      )
      self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase      )					,			[7, 4, 5, 10, 8, 9]      )





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      if not self.test_rust_tokenizer:
         return

      a__	:						Tuple      		=  self.get_tokenizer()
      a__	:						int      		=  self.get_rust_tokenizer()

      a__	:						Tuple      		=  "I was born in 92000, and this is falsé."

      a__	:						Tuple      		=  tokenizer.tokenize(_UpperCAmelCase      )
      a__	:						Union[str, Any]      		=  rust_tokenizer.tokenize(_UpperCAmelCase      )
      self.assertListEqual(_UpperCAmelCase					,			_UpperCAmelCase      )

      a__	:						Dict      		=  tokenizer.encode(_UpperCAmelCase					,			add_special_tokens=_UpperCAmelCase      )
      a__	:						Optional[Any]      		=  rust_tokenizer.encode(_UpperCAmelCase					,			add_special_tokens=_UpperCAmelCase      )
      self.assertListEqual(_UpperCAmelCase					,			_UpperCAmelCase      )

      a__	:						List[Any]      		=  self.get_rust_tokenizer()
      a__	:						Any      		=  tokenizer.encode(_UpperCAmelCase      )
      a__	:						Any      		=  rust_tokenizer.encode(_UpperCAmelCase      )
      self.assertListEqual(_UpperCAmelCase					,			_UpperCAmelCase      )








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   __lowerCAmelCase			(					_UpperCamelCase		,_UpperCamelCase		,_UpperCamelCase		,unittest.TestCase  ):
   _UpperCamelCase :						Optional[int]     		=				StableUnCLIPPipeline
   _UpperCamelCase :						Optional[int]     		=				TEXT_TO_IMAGE_PARAMS
   _UpperCamelCase :						str     		=				TEXT_TO_IMAGE_BATCH_PARAMS
   _UpperCamelCase :						List[Any]     		=				TEXT_TO_IMAGE_IMAGE_PARAMS
   _UpperCamelCase :						str     		=				TEXT_TO_IMAGE_IMAGE_PARAMS

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





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Any      		=  32
      a__	:						int      		=  embedder_hidden_size

      # prior components

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

      torch.manual_seed(0      )
      a__	:						Optional[Any]      		=  CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0					,			eos_token_id=2					,			hidden_size=snake_case					,			projection_dim=snake_case					,			intermediate_size=37					,			layer_norm_eps=1E-05					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=1_000					,			)      )

      torch.manual_seed(0      )
      a__	:						int      		=  PriorTransformer(
          num_attention_heads=2					,			attention_head_dim=12					,			embedding_dim=snake_case					,			num_layers=1					,			)

      torch.manual_seed(0      )
      a__	:						str      		=  DDPMScheduler(
          variance_type="fixed_small_log"					,			prediction_type="sample"					,			num_train_timesteps=1_000					,			clip_sample=snake_case					,			clip_sample_range=5.0					,			beta_schedule="squaredcos_cap_v2"					,			)

      # regular denoising components

      torch.manual_seed(0      )
      a__	:						Any      		=  StableUnCLIPImageNormalizer(embedding_dim=snake_case      )
      a__	:						int      		=  DDPMScheduler(beta_schedule="squaredcos_cap_v2"      )

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

      torch.manual_seed(0      )
      a__	:						Union[str, Any]      		=  CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0					,			eos_token_id=2					,			hidden_size=snake_case					,			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=1_000					,			)      )

      torch.manual_seed(0      )
      a__	:						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=snake_case					,			layers_per_block=1					,			upcast_attention=snake_case					,			use_linear_projection=snake_case					,			)

      torch.manual_seed(0      )
      a__	:						Tuple      		=  DDIMScheduler(
          beta_schedule="scaled_linear"					,			beta_start=0.00_085					,			beta_end=0.012					,			prediction_type="v_prediction"					,			set_alpha_to_one=snake_case					,			steps_offset=1					,			)

      torch.manual_seed(0      )
      a__	:						Optional[int]      		=  AutoencoderKL()

      a__	:						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 	_snake_case ( self					,			snake_case					,			snake_case=0      )				->  Dict:
      """simple docstring"""





      if str(snake_case      ).startswith("mps"      ):
         a__	:						Union[str, Any]      		=  torch.manual_seed(snake_case      )
      else:
         a__	:						List[str]      		=  torch.Generator(device=snake_case      ).manual_seed(snake_case      )
      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 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Dict      		=  torch_device == "cpu"

      self._test_attention_slicing_forward_pass(test_max_difference=snake_case      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						int      		=  torch_device in ["cpu", "mps"]

      self._test_inference_batch_single_identical(test_max_difference=snake_case      )







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





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





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





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						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"      )

      a__	:						int      		=  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"					,			torch_dtype=torch.floataa      )
      pipe.to(snake_case      )
      pipe.set_progress_bar_config(disable=snake_case      )
      # 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__	:						Dict      		=  torch.Generator(device="cpu"      ).manual_seed(0      )
      a__	:						Dict      		=  pipe("anime turle"					,			generator=snake_case					,			output_type="np"      )

      a__	:						List[str]      		=  output.images[0]

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

      assert_mean_pixel_difference(snake_case					,			snake_case      )





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





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

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

      a__	:						Union[str, Any]      		=  pipe(
          "anime turtle"					,			prior_num_inference_steps=2					,			num_inference_steps=2					,			output_type="np"					,			)

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

