





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase		,   unittest.TestCase   ):

  """simple docstring"""


  a_      =			StableDiffusionSAGPipeline
  a_      =			TEXT_TO_IMAGE_PARAMS
  a_      =			TEXT_TO_IMAGE_BATCH_PARAMS
  a_      =			TEXT_TO_IMAGE_IMAGE_PARAMS
  a_      =			TEXT_TO_IMAGE_IMAGE_PARAMS
  a_      =			False






  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDConditionModel(
         block_out_channels=(3_2, 6_4) ,  layers_per_block=2 ,  sample_size=3_2 ,  in_channels=4 ,  out_channels=4 ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,  cross_attention_dim=3_2 ,  )
     __lowerCAmelCase							     =     DDIMScheduler(
         beta_start=0.0_00_85 ,  beta_end=0.0_12 ,  beta_schedule='scaled_linear' ,  clip_sample=lowerCAmelCase_ ,  set_alpha_to_one=lowerCAmelCase_ ,  )
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     AutoencoderKL(
         block_out_channels=[3_2, 6_4] ,  in_channels=3 ,  out_channels=3 ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,  latent_channels=4 ,  )
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     CLIPTextConfig(
         bos_token_id=0 ,  eos_token_id=2 ,  hidden_size=3_2 ,  intermediate_size=3_7 ,  layer_norm_eps=1e-05 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  pad_token_id=1 ,  vocab_size=1_0_0_0 ,  )
     __lowerCAmelCase							     =     CLIPTextModel(lowerCAmelCase_					)
     __lowerCAmelCase							     =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

     __lowerCAmelCase							     =     {
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'safety_checker': None,
         'feature_extractor': None,
     }
     return components






  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		int=0					)							->						Dict:
     if str(lowerCAmelCase_					).startswith('mps'					):
        __lowerCAmelCase							     =     torch.manual_seed(lowerCAmelCase_					)
     else:
        __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
     __lowerCAmelCase							     =     {
         'prompt': '.',
         'generator': generator,
         'num_inference_steps': 2,
         'guidance_scale': 1.0,
         'sag_scale': 1.0,
         'output_type': 'numpy',
     }
     return inputs






  def     lowercase		(    self			:		int					)							->						List[Any]:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3					)





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

  """simple docstring"""


  def     lowercase		(    self			:		Optional[int]					)							->						Union[str, Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def     lowercase		(    self			:		Optional[int]					)							->						List[Any]:
     __lowerCAmelCase							     =     StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'					)
     __lowerCAmelCase							     =     sag_pipe.to(lowerCAmelCase_					)
     sag_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     '.'
     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     sag_pipe(
         [prompt] ,  generator=lowerCAmelCase_ ,  guidance_scale=7.5 ,  sag_scale=1.0 ,  num_inference_steps=2_0 ,  output_type='np'					)

     __lowerCAmelCase							     =     output.images

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

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase							     =     np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-2






  def     lowercase		(    self			:		Tuple					)							->						Optional[Any]:
     __lowerCAmelCase							     =     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'					)
     __lowerCAmelCase							     =     sag_pipe.to(lowerCAmelCase_					)
     sag_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     '.'
     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     sag_pipe(
         [prompt] ,  generator=lowerCAmelCase_ ,  guidance_scale=7.5 ,  sag_scale=1.0 ,  num_inference_steps=2_0 ,  output_type='np'					)

     __lowerCAmelCase							     =     output.images

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

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase							     =     np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-2






  def     lowercase		(    self			:		Union[str, Any]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'					)
     __lowerCAmelCase							     =     sag_pipe.to(lowerCAmelCase_					)
     sag_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     '.'
     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     sag_pipe(
         [prompt] ,  width=7_6_8 ,  height=5_1_2 ,  generator=lowerCAmelCase_ ,  guidance_scale=7.5 ,  sag_scale=1.0 ,  num_inference_steps=2_0 ,  output_type='np' ,  )

     __lowerCAmelCase							     =     output.images

     assert image.shape == (1, 5_1_2, 7_6_8, 3)








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   a_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Tuple     ):
   # Initialise PyTorch model.
   # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
   # TapasConfig to False.

   # initialize configuration from json file
   __lowerCAmelCase							     =     TapasConfig.from_json_file(lowerCAmelCase_     )
   # set absolute/relative position embeddings parameter
   __lowerCAmelCase							     =     reset_position_index_per_cell

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

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

      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "TABFACT":
      __lowerCAmelCase							     =     TapasForSequenceClassification(config=lowerCAmelCase_     )
   elif task == "MLM":
      __lowerCAmelCase							     =     TapasForMaskedLM(config=lowerCAmelCase_     )
   elif task == "INTERMEDIATE_PRETRAINING":
      __lowerCAmelCase							     =     TapasModel(config=lowerCAmelCase_     )
   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(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )

   # Save pytorch-model (weights and configuration)
   print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
   model.save_pretrained(lowerCAmelCase_     )

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

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


if __name__ == "__main__":
     _snake_case  :   Union[str, Any]						   =  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.'
     )
     _snake_case  :   Optional[Any]						   =  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,
     )

