

import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    # FIXME: add fast tests
    pass




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

    @property
    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )

    @property
    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			ort.SessionOptions()
     SCREAMING_SNAKE_CASE								=			False
     return options

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'    )
     SCREAMING_SNAKE_CASE								=			load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'    )
     SCREAMING_SNAKE_CASE								=			OnnxStableDiffusionInpaintPipeline.from_pretrained(
         'runwayml/stable-diffusion-inpainting'							,    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__    )

     SCREAMING_SNAKE_CASE								=			'A red cat sitting on a park bench'

     SCREAMING_SNAKE_CASE								=			np.random.RandomState(0    )
     SCREAMING_SNAKE_CASE								=			pipe(
         prompt=snake_case__							,    image=snake_case__							,    mask_image=snake_case__							,    guidance_scale=7.5							,    num_inference_steps=1_0							,    generator=snake_case__							,    output_type='np'							,    )
     SCREAMING_SNAKE_CASE								=			output.images
     SCREAMING_SNAKE_CASE								=			images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

     assert images.shape == (1, 5_1_2, 5_1_2, 3)
     SCREAMING_SNAKE_CASE								=			np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]    )

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




    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'    )
     SCREAMING_SNAKE_CASE								=			load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'    )
     SCREAMING_SNAKE_CASE								=			LMSDiscreteScheduler.from_pretrained(
         'runwayml/stable-diffusion-inpainting'							,    subfolder='scheduler'							,    revision='onnx'    )
     SCREAMING_SNAKE_CASE								=			OnnxStableDiffusionInpaintPipeline.from_pretrained(
         'runwayml/stable-diffusion-inpainting'							,    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__    )

     SCREAMING_SNAKE_CASE								=			'A red cat sitting on a park bench'

     SCREAMING_SNAKE_CASE								=			np.random.RandomState(0    )
     SCREAMING_SNAKE_CASE								=			pipe(
         prompt=snake_case__							,    image=snake_case__							,    mask_image=snake_case__							,    guidance_scale=7.5							,    num_inference_steps=2_0							,    generator=snake_case__							,    output_type='np'							,    )
     SCREAMING_SNAKE_CASE								=			output.images
     SCREAMING_SNAKE_CASE								=			images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

     assert images.shape == (1, 5_1_2, 5_1_2, 3)
     SCREAMING_SNAKE_CASE								=			np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]    )

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




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


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

a_    :						Optional[Any]    		=      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_    :						Any    		=      {
    "vocab_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
    },
    "merges_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
    },
}

a_    :						Union[str, Any]    		=      {"allegro/herbert-base-cased": 514}
a_    :						List[Any]    		=      {}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =PRETRAINED_INIT_CONFIGURATION
    __UpperCamelCase         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =HerbertTokenizer

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

     """simple docstring"""






     super().__init__(
         snake_case__							,    snake_case__							,    tokenizer_file=snake_case__							,    cls_token=snake_case__							,    unk_token=snake_case__							,    pad_token=snake_case__							,    mask_token=snake_case__							,    sep_token=snake_case__							,    **snake_case__							,    )

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.cls_token_id]
     SCREAMING_SNAKE_CASE								=			[self.sep_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 UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None							,    snake_case__ :      bool = False    ):

     """simple docstring"""






     if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=snake_case__							,    token_ids_a=snake_case__							,    already_has_special_tokens=snake_case__    )

     if token_ids_a is None:
      return [1] + ([0] * len(snake_case__    )) + [1]
     return [1] + ([0] * len(snake_case__    )) + [1] + ([0] * len(snake_case__    )) + [1]

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.sep_token_id]
     SCREAMING_SNAKE_CASE								=			[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 UpperCamelCase			(			self :      Tuple							,    snake_case__ :      str							,    snake_case__ :      Optional[str] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self._tokenizer.model.save(snake_case__							,    name=snake_case__    )
     return tuple(snake_case__    )

