


'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

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


enable_full_determinism()






class    __snake_case       (   _SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE	,unittest.TestCase):







    """simple docstring"""



    lowercase    =					StableUnCLIPImgaImgPipeline
    lowercase    =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    lowercase    =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    lowercase    =					frozenset(
        [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    lowercase    =					frozenset([])

    def __lowercase   (   self    :  Union[str, Any]		)   ->  Any:
      lowerCAmelCase_			:      str			=   32
      lowerCAmelCase_			:      Dict			=   embedder_hidden_size

      # image encoding components

      lowerCAmelCase_			:      int			=   CLIPImageProcessor(crop_size=32    ,						size=32		)

      torch.manual_seed(0		)
      lowerCAmelCase_			:      int			=   CLIPVisionModelWithProjection(
          CLIPVisionConfig(
              hidden_size=lowerCamelCase    ,						projection_dim=lowerCamelCase    ,						num_hidden_layers=5    ,						num_attention_heads=4    ,						image_size=32    ,						intermediate_size=37    ,						patch_size=1    ,						)		)

      # regular denoising components

      torch.manual_seed(0		)
      lowerCAmelCase_			:      List[Any]			=   StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""		)

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

      torch.manual_seed(0		)
      lowerCAmelCase_			:      Any			=   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=10_00    ,						)		)

      torch.manual_seed(0		)
      lowerCAmelCase_			:      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=lowerCamelCase    ,						layers_per_block=1    ,						upcast_attention=lowerCamelCase    ,						use_linear_projection=lowerCamelCase    ,						)

      torch.manual_seed(0		)
      lowerCAmelCase_			:      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		)
      lowerCAmelCase_			:      Dict			=   AutoencoderKL()

      lowerCAmelCase_			:      str			=   {
          # image encoding components
          """feature_extractor""": feature_extractor,
          """image_encoder""": image_encoder.eval(),
          # image noising components
          """image_normalizer""": image_normalizer.eval(),
          """image_noising_scheduler""": image_noising_scheduler,
          # regular denoising components
          """tokenizer""": tokenizer,
          """text_encoder""": text_encoder.eval(),
          """unet""": unet.eval(),
          """scheduler""": scheduler,
          """vae""": vae.eval(),
      }

      return components

    def __lowercase   (   self    :  Optional[int]    ,						lowerCamelCase    :  List[Any]    ,						lowerCamelCase    :  Any=0    ,						lowerCamelCase    :  Union[str, Any]=True		)   ->  List[str]:
      if str(lowerCamelCase		).startswith("""mps"""		):
        lowerCAmelCase_			:      Optional[Any]			=   torch.manual_seed(lowerCamelCase		)
      else:
        lowerCAmelCase_			:      Union[str, Any]			=   torch.Generator(device=lowerCamelCase		).manual_seed(lowerCamelCase		)

      lowerCAmelCase_			:      str			=   floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(lowerCamelCase		)		).to(lowerCamelCase		)

      if pil_image:
        lowerCAmelCase_			:      str			=   input_image * 0.5 + 0.5
        lowerCAmelCase_			:      Union[str, Any]			=   input_image.clamp(0    ,						1		)
        lowerCAmelCase_			:      List[str]			=   input_image.cpu().permute(0    ,						2    ,						3    ,						1		).float().numpy()
        lowerCAmelCase_			:      List[str]			=   DiffusionPipeline.numpy_to_pil(lowerCamelCase		)[0]

      return {
          "prompt": "An anime racoon running a marathon",
          "image": input_image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "np",
      }

    @skip_mps
    def __lowercase   (   self    :  Any		)   ->  Union[str, Any]:
      lowerCAmelCase_			:      Tuple			=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowerCAmelCase_			:      Union[str, Any]			=   self.get_dummy_components()
      lowerCAmelCase_			:      List[str]			=   StableUnCLIPImgaImgPipeline(**lowerCamelCase		)
      lowerCAmelCase_			:      List[str]			=   sd_pipe.to(lowerCamelCase		)
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_			:      Dict			=   self.get_dummy_inputs(lowerCamelCase		)
      inputs.update({"""image_embeds""": None}		)
      lowerCAmelCase_			:      List[Any]			=   sd_pipe(**lowerCamelCase		).images
      lowerCAmelCase_			:      Any			=   image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_			:      Tuple			=   np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3

    def __lowercase   (   self    :  Any		)   ->  Dict:
      lowerCAmelCase_			:      Optional[int]			=   torch_device in ["""cpu""", """mps"""]

      self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase		)

    def __lowercase   (   self    :  Optional[Any]		)   ->  Tuple:
      lowerCAmelCase_			:      List[str]			=   torch_device in ["""cpu""", """mps"""]

      self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase		)





    @unittest.skipIf(
        torch_device != """cuda""" or not is_xformers_available()    ,						reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,						)
    def __lowercase   (   self    :  Dict		)   ->  List[Any]:
      self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowerCamelCase		)







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







    """simple docstring"""



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

    def __lowercase   (   self    :  List[Any]		)   ->  Tuple:
      lowerCAmelCase_			:      Any			=   load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""		)

      lowerCAmelCase_			:      str			=   load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"""		)

      lowerCAmelCase_			:      Union[str, Any]			=   StableUnCLIPImgaImgPipeline.from_pretrained(
          """fusing/stable-unclip-2-1-l-img2img"""    ,						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()

      lowerCAmelCase_			:      Optional[int]			=   torch.Generator(device="""cpu"""		).manual_seed(0		)
      lowerCAmelCase_			:      Any			=   pipe(lowerCamelCase    ,						"""anime turle"""    ,						generator=lowerCamelCase    ,						output_type="""np"""		)

      lowerCAmelCase_			:      int			=   output.images[0]

      assert image.shape == (7_68, 7_68, 3)

      assert_mean_pixel_difference(lowerCamelCase    ,						lowerCamelCase		)

    def __lowercase   (   self    :  List[Any]		)   ->  str:
      lowerCAmelCase_			:      Dict			=   load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""		)

      lowerCAmelCase_			:      Optional[int]			=   load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"""		)

      lowerCAmelCase_			:      Optional[int]			=   StableUnCLIPImgaImgPipeline.from_pretrained(
          """fusing/stable-unclip-2-1-h-img2img"""    ,						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()

      lowerCAmelCase_			:      int			=   torch.Generator(device="""cpu"""		).manual_seed(0		)
      lowerCAmelCase_			:      Any			=   pipe(lowerCamelCase    ,						"""anime turle"""    ,						generator=lowerCamelCase    ,						output_type="""np"""		)

      lowerCAmelCase_			:      Dict			=   output.images[0]

      assert image.shape == (7_68, 7_68, 3)

      assert_mean_pixel_difference(lowerCamelCase    ,						lowerCamelCase		)





    def __lowercase   (   self    :  Dict		)   ->  List[str]:
      lowerCAmelCase_			:      Union[str, Any]			=   load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""		)

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

      lowerCAmelCase_			:      Any			=   StableUnCLIPImgaImgPipeline.from_pretrained(
          """fusing/stable-unclip-2-1-h-img2img"""    ,						torch_dtype=torch.floataa		)
      lowerCAmelCase_			:      List[Any]			=   pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      lowerCAmelCase_			:      str			=   pipe(
          lowerCamelCase    ,						"""anime turtle"""    ,						num_inference_steps=2    ,						output_type="""np"""    ,						)

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







'''simple docstring'''






import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class    __snake_case       (   _SCREAMING_SNAKE_CASE	,unittest.TestCase):







    """simple docstring"""



    lowercase    =					BioGptTokenizer
    lowercase    =					False

    def __lowercase   (   self    :  List[Any]		)   ->  List[str]:
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      lowerCAmelCase_			:      Optional[Any]			=   [
          """l""",
          """o""",
          """w""",
          """e""",
          """r""",
          """s""",
          """t""",
          """i""",
          """d""",
          """n""",
          """w</w>""",
          """r</w>""",
          """t</w>""",
          """lo""",
          """low""",
          """er</w>""",
          """low</w>""",
          """lowest</w>""",
          """newer</w>""",
          """wider</w>""",
          """<unk>""",
      ]
      lowerCAmelCase_			:      int			=   dict(zip(lowerCamelCase    ,						range(len(lowerCamelCase		)		)		)		)
      lowerCAmelCase_			:      int			=   ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

      lowerCAmelCase_			:      Optional[int]			=   os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""vocab_file"""]		)
      lowerCAmelCase_			:      Union[str, Any]			=   os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""merges_file"""]		)
      with open(self.vocab_file    ,						"""w"""		) as fp:
        fp.write(json.dumps(lowerCamelCase		)		)
      with open(self.merges_file    ,						"""w"""		) as fp:
        fp.write("""\n""".join(lowerCamelCase		)		)

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  Any		)   ->  List[str]:
      lowerCAmelCase_			:      Dict			=   """lower newer"""
      lowerCAmelCase_			:      str			=   """lower newer"""
      return input_text, output_text

    def __lowercase   (   self    :  Optional[int]		)   ->  str:
      lowerCAmelCase_			:      Any			=   BioGptTokenizer(self.vocab_file    ,						self.merges_file		)

      lowerCAmelCase_			:      int			=   """lower"""
      lowerCAmelCase_			:      str			=   ["""low""", """er</w>"""]
      lowerCAmelCase_			:      Any			=   tokenizer.tokenize(lowerCamelCase		)
      self.assertListEqual(lowerCamelCase    ,						lowerCamelCase		)

      lowerCAmelCase_			:      Dict			=   tokens + ["""<unk>"""]
      lowerCAmelCase_			:      Optional[Any]			=   [14, 15, 20]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase		)    ,						lowerCamelCase		)





    @slow
    def __lowercase   (   self    :  str		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Dict			=   BioGptTokenizer.from_pretrained("""microsoft/biogpt"""		)

      lowerCAmelCase_			:      int			=   tokenizer.encode("""sequence builders"""    ,						add_special_tokens=lowerCamelCase		)
      lowerCAmelCase_			:      Optional[Any]			=   tokenizer.encode("""multi-sequence build"""    ,						add_special_tokens=lowerCamelCase		)

      lowerCAmelCase_			:      str			=   tokenizer.build_inputs_with_special_tokens(lowerCamelCase		)
      lowerCAmelCase_			:      Optional[int]			=   tokenizer.build_inputs_with_special_tokens(lowerCamelCase    ,						lowerCamelCase		)

      self.assertTrue(encoded_sentence == [2] + text		)
      self.assertTrue(encoded_pair == [2] + text + [2] + text_a		)



