




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase__					:  List[str]      							=				{
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Optional[int]      							=				['LayoutLMv2TokenizerFast']

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  int      							=				['LayoutLMv2FeatureExtractor']
   lowerCamelCase__					:  List[Any]      							=				['LayoutLMv2ImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  List[str]      							=				[
       'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'LayoutLMv2ForQuestionAnswering',
       'LayoutLMv2ForSequenceClassification',
       'LayoutLMv2ForTokenClassification',
       'LayoutLMv2Layer',
       'LayoutLMv2Model',
       'LayoutLMv2PreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
   from .processing_layoutlmva import LayoutLMvaProcessor
   from .tokenization_layoutlmva import LayoutLMvaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlmva import (
          LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMvaForQuestionAnswering,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaLayer,
          LayoutLMvaModel,
          LayoutLMvaPreTrainedModel,
      )
else:
   import sys

   lowerCamelCase__					:  int      							=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



"""simple docstring"""


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
@slow
@require_torch_gpu
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							def      snake_case							(	self      ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_euler"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe([prompt]       ,  generator=__a       ,  guidance_scale=9.0       ,  num_inference_steps=20       ,  output_type="np"      )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_euler"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe([prompt]       ,  generator=__a       ,  guidance_scale=9.0       ,  num_inference_steps=20       ,  output_type="np"      )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-1



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_dpmpp_2m"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe(
										    [prompt]       ,  generator=__a       ,  guidance_scale=7.5       ,  num_inference_steps=15       ,  output_type="np"       ,  use_karras_sigmas=__a       ,  )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array(
										    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2




