



'''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:
     lowerCAmelCase:      Tuple		 =		None

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

lowerCAmelCase:      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''',
    },
}

lowerCAmelCase:      str		 =		{
    '''albert-base-v1''': 5_1_2,
    '''albert-large-v1''': 5_1_2,
    '''albert-xlarge-v1''': 5_1_2,
    '''albert-xxlarge-v1''': 5_1_2,
    '''albert-base-v2''': 5_1_2,
    '''albert-large-v2''': 5_1_2,
    '''albert-xlarge-v2''': 5_1_2,
    '''albert-xxlarge-v2''': 5_1_2,
}

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

class 							a__(      a__			):
    lowercase__				=					VOCAB_FILES_NAMES
    lowercase__				=					PRETRAINED_VOCAB_FILES_MAP
    lowercase__				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__				=					AlbertTokenizer







    def __init__(   self				:						Dict		,    __snake_case				:						int=None		,    __snake_case				:						Dict=None		,    __snake_case				:						Optional[Any]=True		,    __snake_case				:						Dict=True		,    __snake_case				:						Optional[Any]=False		,    __snake_case				:						Optional[Any]="[CLS]"		,    __snake_case				:						int="[SEP]"		,    __snake_case				:						Dict="<unk>"		,    __snake_case				:						Optional[Any]="[SEP]"		,    __snake_case				:						Tuple="<pad>"		,    __snake_case				:						Union[str, Any]="[CLS]"		,    __snake_case				:						Union[str, Any]="[MASK]"		,    **__snake_case				:						Tuple		,    ):
      # 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     lowercase_ (   self				:						Any		,    __snake_case				:						List[Any]		,    __snake_case				:						str = None			):
      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     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Any		,    __snake_case				:						List[str] = None			):
      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     lowercase_ (   self				:						Tuple		,    __snake_case				:						List[str]		,    __snake_case				:						List[Any] = None			):
      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,)




'''simple docstring'''






import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
     import onnxruntime as ort

class 							a__(      lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Union[str, Any]=0			):
      a		:      List[str]       =  floats_tensor((1, 3, 1_28, 1_28)		,    rng=random.Random(__snake_case			)			)
      a		:      Optional[Any]       =  np.random.RandomState(__snake_case			)
      a		:      List[str]       =  {
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 3,
          'strength': 0.75,
          'guidance_scale': 7.5,
          'output_type': 'numpy',
      }
      return inputs







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      Dict       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Any       =  self.get_dummy_inputs()
      a		:      int       =  pipe(**__snake_case			).images
      a		:      List[Any]       =  image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Dict       =  np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]			)
      assert np.abs(image_slice - expected_slice			).max() < 1e-1







    def     lowercase_ (   self				:						str			):
      a		:      List[Any]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Dict       =  PNDMScheduler.from_config(pipe.scheduler.config		,    skip_prk_steps=__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Any       =  self.get_dummy_inputs()
      a		:      str       =  pipe(**__snake_case			).images
      a		:      Union[str, Any]       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      int       =  np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]			)

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







    def     lowercase_ (   self				:						List[str]			):
      a		:      Optional[int]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      List[Any]       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      # warmup pass to apply optimizations
      a		:      Any       =  pipe(**self.get_dummy_inputs()			)

      a		:      Optional[int]       =  self.get_dummy_inputs()
      a		:      Optional[int]       =  pipe(**__snake_case			).images
      a		:      Any       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Any       =  np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]			)

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







    def     lowercase_ (   self				:						List[Any]			):
      a		:      Dict       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Any       =  EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      List[str]       =  self.get_dummy_inputs()
      a		:      Optional[int]       =  pipe(**__snake_case			).images
      a		:      Any       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Dict       =  np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]			)

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







    def     lowercase_ (   self				:						Tuple			):
      a		:      Any       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Optional[Any]       =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Dict       =  self.get_dummy_inputs()
      a		:      Union[str, Any]       =  pipe(**__snake_case			).images
      a		:      int       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      int       =  np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]			)

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







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      Optional[int]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Optional[int]       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Tuple       =  self.get_dummy_inputs()
      a		:      Optional[Any]       =  pipe(**__snake_case			).images
      a		:      str       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Optional[int]       =  np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]			)

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

@nightly
@require_onnxruntime
@require_torch_gpu
class 							a__(      unittest.TestCase			):







    @property
    def     lowercase_ (   self				:						List[Any]			):
      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )







    @property
    def     lowercase_ (   self				:						Dict			):
      a		:      Tuple       =  ort.SessionOptions()
      a		:      Optional[Any]       =  False
      return options







    def     lowercase_ (   self				:						List[str]			):
      a		:      Dict       =  load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/img2img/sketch-mountains-input.jpg'			)
      a		:      Optional[int]       =  init_image.resize((7_68, 5_12)			)
      # using the PNDM scheduler by default
      a		:      Any       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          'CompVis/stable-diffusion-v1-4'		,    revision='onnx'		,    safety_checker=__snake_case		,    feature_extractor=__snake_case		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Union[str, Any]       =  'A fantasy landscape, trending on artstation'

      a		:      Dict       =  np.random.RandomState(0			)
      a		:      Optional[int]       =  pipe(
          prompt=__snake_case		,    image=__snake_case		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=10		,    generator=__snake_case		,    output_type='np'		,    )
      a		:      str       =  output.images
      a		:      Optional[int]       =  images[0, 2_55:2_58, 3_83:3_86, -1]

      assert images.shape == (1, 5_12, 7_68, 3)
      a		:      List[str]       =  np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]			)
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







    def     lowercase_ (   self				:						str			):
      a		:      Optional[Any]       =  load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/img2img/sketch-mountains-input.jpg'			)
      a		:      List[str]       =  init_image.resize((7_68, 5_12)			)
      a		:      Union[str, Any]       =  LMSDiscreteScheduler.from_pretrained(
          'runwayml/stable-diffusion-v1-5'		,    subfolder='scheduler'		,    revision='onnx'			)
      a		:      Any       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'		,    revision='onnx'		,    scheduler=__snake_case		,    safety_checker=__snake_case		,    feature_extractor=__snake_case		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Optional[int]       =  'A fantasy landscape, trending on artstation'

      a		:      str       =  np.random.RandomState(0			)
      a		:      List[str]       =  pipe(
          prompt=__snake_case		,    image=__snake_case		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=20		,    generator=__snake_case		,    output_type='np'		,    )
      a		:      str       =  output.images
      a		:      Tuple       =  images[0, 2_55:2_58, 3_83:3_86, -1]

      assert images.shape == (1, 5_12, 7_68, 3)
      a		:      Union[str, Any]       =  np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]			)
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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