



'''simple docstring'''





import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, 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						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



 def 					a__  (self ,   A=0       )    ->     Tuple:


      """simple docstring"""



      _a      =  np.random.RandomState(snake_case_       )
      _a      =  {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 7.5,
          '''output_type''': '''numpy''',
      }
      return inputs



 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      _a      =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,   provider='''CPUExecutionProvider'''       )
      pipe.set_progress_bar_config(disable=snake_case_       )

      _a      =  self.get_dummy_inputs()
      _a      =  pipe(**snake_case_       ).images
      _a      =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a      =  np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455]       )

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



 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,   provider='''CPUExecutionProvider'''       )
      _a      =  PNDMScheduler.from_config(pipe.scheduler.config ,   skip_prk_steps=snake_case_       )
      pipe.set_progress_bar_config(disable=snake_case_       )

      _a      =  self.get_dummy_inputs()
      _a      =  pipe(**snake_case_       ).images
      _a      =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a      =  np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330]       )

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



 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      _a      =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,   provider='''CPUExecutionProvider'''       )
      _a      =  LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
      pipe.set_progress_bar_config(disable=snake_case_       )

      _a      =  self.get_dummy_inputs()
      _a      =  pipe(**snake_case_       ).images
      _a      =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a      =  np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]       )

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



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      _a      =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,   provider='''CPUExecutionProvider'''       )
      _a      =  EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
      pipe.set_progress_bar_config(disable=snake_case_       )

      _a      =  self.get_dummy_inputs()
      _a      =  pipe(**snake_case_       ).images
      _a      =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a      =  np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]       )

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



 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



      _a      =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,   provider='''CPUExecutionProvider'''       )
      _a      =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
      pipe.set_progress_bar_config(disable=snake_case_       )

      _a      =  self.get_dummy_inputs()
      _a      =  pipe(**snake_case_       ).images
      _a      =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a      =  np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271]       )

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



 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,   provider='''CPUExecutionProvider'''       )
      _a      =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
      pipe.set_progress_bar_config(disable=snake_case_       )

      _a      =  self.get_dummy_inputs()
      _a      =  pipe(**snake_case_       ).images
      _a      =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a      =  np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200]       )

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



 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,   provider='''CPUExecutionProvider'''       )
      pipe.set_progress_bar_config(disable=snake_case_       )

      _a      =  self.get_dummy_inputs()
      _a      =  3 * [inputs['''prompt''']]

      # forward
      _a      =  pipe(**snake_case_       )
      _a      =  output.images[0, -3:, -3:, -1]

      _a      =  self.get_dummy_inputs()
      _a      =  3 * [inputs.pop('''prompt'''       )]

      _a      =  pipe.tokenizer(
          snake_case_ ,   padding='''max_length''' ,   max_length=pipe.tokenizer.model_max_length ,   truncation=snake_case_ ,   return_tensors='''np''' ,   )
      _a      =  text_inputs['''input_ids''']

      _a      =  pipe.text_encoder(input_ids=text_inputs.astype(np.intaa       )       )[0]

      _a      =  prompt_embeds

      # forward
      _a      =  pipe(**snake_case_       )
      _a      =  output.images[0, -3:, -3:, -1]

      assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1E-4



 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,   provider='''CPUExecutionProvider'''       )
      pipe.set_progress_bar_config(disable=snake_case_       )

      _a      =  self.get_dummy_inputs()
      _a      =  3 * ['''this is a negative prompt''']
      _a      =  negative_prompt
      _a      =  3 * [inputs['''prompt''']]

      # forward
      _a      =  pipe(**snake_case_       )
      _a      =  output.images[0, -3:, -3:, -1]

      _a      =  self.get_dummy_inputs()
      _a      =  3 * [inputs.pop('''prompt'''       )]

      _a      =  []
      for p in [prompt, negative_prompt]:
           _a      =  pipe.tokenizer(
               snake_case_ ,   padding='''max_length''' ,   max_length=pipe.tokenizer.model_max_length ,   truncation=snake_case_ ,   return_tensors='''np''' ,   )
           _a      =  text_inputs['''input_ids''']

           embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa       )       )[0]       )

      _a  ,    _a      =  embeds

      # forward
      _a      =  pipe(**snake_case_       )
      _a      =  output.images[0, -3:, -3:, -1]

      assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1E-4






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



 '''simple docstring'''







 @property
 def 					a__  (self       )    ->     str:


      """simple docstring"""



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



 @property
 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



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



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  OnnxStableDiffusionPipeline.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 ,   )
      sd_pipe.set_progress_bar_config(disable=snake_case_       )

      _a      =  '''A painting of a squirrel eating a burger'''
      np.random.seed(0       )
      _a      =  sd_pipe([prompt] ,   guidance_scale=6.0 ,   num_inference_steps=10 ,   output_type='''np'''       )
      _a      =  output.images

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

      assert image.shape == (1, 512, 512, 3)
      _a      =  np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]       )

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



 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      _a      =  DDIMScheduler.from_pretrained(
          '''runwayml/stable-diffusion-v1-5''' ,   subfolder='''scheduler''' ,   revision='''onnx'''       )
      _a      =  OnnxStableDiffusionPipeline.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 ,   )
      sd_pipe.set_progress_bar_config(disable=snake_case_       )

      _a      =  '''open neural network exchange'''
      _a      =  np.random.RandomState(0       )
      _a      =  sd_pipe([prompt] ,   guidance_scale=7.5 ,   num_inference_steps=10 ,   generator=snake_case_ ,   output_type='''np'''       )
      _a      =  output.images
      _a      =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      _a      =  np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]       )

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



 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      _a      =  LMSDiscreteScheduler.from_pretrained(
          '''runwayml/stable-diffusion-v1-5''' ,   subfolder='''scheduler''' ,   revision='''onnx'''       )
      _a      =  OnnxStableDiffusionPipeline.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 ,   )
      sd_pipe.set_progress_bar_config(disable=snake_case_       )

      _a      =  '''open neural network exchange'''
      _a      =  np.random.RandomState(0       )
      _a      =  sd_pipe([prompt] ,   guidance_scale=7.5 ,   num_inference_steps=10 ,   generator=snake_case_ ,   output_type='''np'''       )
      _a      =  output.images
      _a      =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      _a      =  np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]       )

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



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      _a      =  0

      def test_callback_fn(A ,   A ,   A       ) -> None:
           _a      =  True
           nonlocal number_of_steps
           number_of_steps += 1
           if step == 0:
                assert latents.shape == (1, 4, 64, 64)
                _a      =  latents[0, -3:, -3:, -1]
                _a      =  np.array(
                    [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]       )

                assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 1E-3
           elif step == 5:
                assert latents.shape == (1, 4, 64, 64)
                _a      =  latents[0, -3:, -3:, -1]
                _a      =  np.array(
                    [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]       )

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

      _a      =  False

      _a      =  OnnxStableDiffusionPipeline.from_pretrained(
          '''runwayml/stable-diffusion-v1-5''' ,   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      =  '''Andromeda galaxy in a bottle'''

      _a      =  np.random.RandomState(0       )
      pipe(
          prompt=snake_case_ ,   num_inference_steps=5 ,   guidance_scale=7.5 ,   generator=snake_case_ ,   callback=snake_case_ ,   callback_steps=1 ,   )
      assert test_callback_fn.has_been_called
      assert number_of_steps == 6



 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      _a      =  OnnxStableDiffusionPipeline.from_pretrained(
          '''runwayml/stable-diffusion-v1-5''' ,   revision='''onnx''' ,   safety_checker=snake_case_ ,   feature_extractor=snake_case_ ,   provider=self.gpu_provider ,   sess_options=self.gpu_options ,   )
      assert isinstance(snake_case_ ,   snake_case_       )
      assert pipe.safety_checker is None

      _a      =  pipe('''example prompt''' ,   num_inference_steps=2       ).images[0]
      assert image is not None

      # check that there's no error when saving a pipeline with one of the models being None
      with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(snake_case_       )
           _a      =  OnnxStableDiffusionPipeline.from_pretrained(snake_case_       )

      # sanity check that the pipeline still works
      assert pipe.safety_checker is None
      _a      =  pipe('''example prompt''' ,   num_inference_steps=2       ).images[0]
      assert image is not None










'''simple docstring'''




import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Dict			,     __lowercase							:      str			,     __lowercase							:      Optional[Any]			,     __lowercase							:      str       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   TapasConfig.from_json_file(__lowercase       )
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     							=   reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   True
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   0.66_4694
          _UpperCAmelCase     							=   0.20_7951
          _UpperCAmelCase     							=   0.12_1194
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   False
          _UpperCAmelCase     							=   0.035_2513

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   False
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   36.4519
          _UpperCAmelCase     							=   0.90_3421
          _UpperCAmelCase     							=   222.088
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   0.76_3141

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "TABFACT":
          _UpperCAmelCase     							=   TapasForSequenceClassification(config=__lowercase       )
     elif task == "MLM":
          _UpperCAmelCase     							=   TapasForMaskedLM(config=__lowercase       )
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     							=   TapasModel(config=__lowercase       )
     else:
          raise ValueError(f'Task {task} not supported.'       )

     print(f'Building PyTorch model from configuration: {config}'       )
     # Load weights from tf checkpoint
     load_tf_weights_in_tapas(__lowercase			,     __lowercase			,     __lowercase       )

     # Save pytorch-model (weights and configuration)
     print(f'Save PyTorch model to {pytorch_dump_path}'       )
     model.save_pretrained(__lowercase       )

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'       )
     _UpperCAmelCase     							=   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"			,     model_max_length=512       )
     tokenizer.save_pretrained(__lowercase       )

     print("Used relative position embeddings:"			,     model.config.reset_position_index_per_cell       )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :List[str]       =				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
  )
  parser.add_argument(
      '''--reset_position_index_per_cell''',
      default=False,
      action='''store_true''',
      help='''Whether to use relative position embeddings or not. Defaults to True.''',
  )
  parser.add_argument(
      '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--tapas_config_file''',
      default=None,
      type=str,
      required=True,
      help=(
          '''The config json file corresponding to the pre-trained TAPAS model. \n'''
          '''This specifies the model architecture.'''
      ),
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  __SCREAMING_SNAKE_CASE     :List[str]       =				parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )




