


'''simple docstring'''
import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


def 			_lowercase       (				UpperCamelCase__       :					int			):
 __A  :		List[str]       =			[tensor.shape for tensor in tensor_list]
 return all(shape == shapes[0] for shape in shapes[1:]			)

class 						_lowerCamelCase					(    snake_case_    ,		snake_case_    ,		snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Dict    							=						StableDiffusionLatentUpscalePipeline
 __lowercase				:		List[str]    							=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     '''height''',
     '''width''',
     '''cross_attention_kwargs''',
     '''negative_prompt_embeds''',
     '''prompt_embeds''',
 }
 __lowercase				:		Dict    							=						PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
 __lowercase				:		Optional[Any]    							=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __lowercase				:		Any    							=						frozenset(
     []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __lowercase				:		Tuple    							=						frozenset([]				)

 __lowercase				:		Tuple    							=						True


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			1
  __A  :		Tuple       =			4
  __A  :		List[str]       =			(16, 16)

  __A  :		List[str]       =			floats_tensor((batch_size, num_channels) + sizes      ,      rng=random.Random(0						)						).to(__lowercase						)
  return image


 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Tuple       =			UNetaDConditionModel(
      act_fn='gelu'      ,      attention_head_dim=8      ,      norm_num_groups=__lowercase      ,      block_out_channels=[32, 32, 64, 64]      ,      time_cond_proj_dim=160      ,      conv_in_kernel=1      ,      conv_out_kernel=1      ,      cross_attention_dim=32      ,      down_block_types=(
          'KDownBlock2D',
          'KCrossAttnDownBlock2D',
          'KCrossAttnDownBlock2D',
          'KCrossAttnDownBlock2D',
      )      ,      in_channels=8      ,      mid_block_type=__lowercase      ,      only_cross_attention=__lowercase      ,      out_channels=5      ,      resnet_time_scale_shift='scale_shift'      ,      time_embedding_type='fourier'      ,      timestep_post_act='gelu'      ,      up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')      ,      )
  __A  :		List[str]       =			AutoencoderKL(
      block_out_channels=[32, 32, 64, 64]      ,      in_channels=3      ,      out_channels=3      ,      down_block_types=[
          'DownEncoderBlock2D',
          'DownEncoderBlock2D',
          'DownEncoderBlock2D',
          'DownEncoderBlock2D',
      ]      ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']      ,      latent_channels=4      ,      )
  __A  :		Optional[Any]       =			EulerDiscreteScheduler(prediction_type='sample'						)
  __A  :		Optional[int]       =			CLIPTextConfig(
      bos_token_id=0      ,      eos_token_id=2      ,      hidden_size=32      ,      intermediate_size=37      ,      layer_norm_eps=1E-05      ,      num_attention_heads=4      ,      num_hidden_layers=5      ,      pad_token_id=1      ,      vocab_size=1_000      ,      hidden_act='quick_gelu'      ,      projection_dim=512      ,      )
  __A  :		Union[str, Any]       =			CLIPTextModel(__lowercase						)
  __A  :		Dict       =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

  __A  :		Optional[int]       =			{
      'unet': model.eval(),
      'vae': vae.eval(),
      'scheduler': scheduler,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
  }

  return components


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase=0						):

  """simple docstring"""







  if str(__lowercase						).startswith('mps'						):
   __A  :		Union[str, Any]       =			torch.manual_seed(__lowercase						)
  else:
   __A  :		Optional[Any]       =			torch.Generator(device=__lowercase						).manual_seed(__lowercase						)
  __A  :		Dict       =			{
      'prompt': 'A painting of a squirrel eating a burger',
      'image': self.dummy_image.cpu(),
      'generator': generator,
      'num_inference_steps': 2,
      'output_type': 'numpy',
  }
  return inputs


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			'cpu'

  __A  :		List[str]       =			self.get_dummy_components()
  __A  :		Tuple       =			self.pipeline_class(**__lowercase						)
  pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

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

  self.assertEqual(image.shape      ,      (1, 256, 256, 3)						)
  __A  :		Any       =			np.array(
      [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]						)
  __A  :		int       =			np.abs(image_slice.flatten() - expected_slice						).max()
  self.assertLessEqual(__lowercase      ,      1E-3						)


 def  snake_case__   (      self						):

  """simple docstring"""







  super().test_attention_slicing_forward_pass(expected_max_diff=7E-3						)


 def  snake_case__   (      self						):

  """simple docstring"""







  super().test_cpu_offload_forward_pass(expected_max_diff=3E-3						)


 def  snake_case__   (      self						):

  """simple docstring"""







  super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3						)


 def  snake_case__   (      self						):

  """simple docstring"""







  super().test_inference_batch_single_identical(expected_max_diff=7E-3						)


 def  snake_case__   (      self						):

  """simple docstring"""







  super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3						)


 def  snake_case__   (      self						):

  """simple docstring"""







  super().test_save_load_local(expected_max_difference=3E-3						)


 def  snake_case__   (      self						):

  """simple docstring"""







  super().test_save_load_optional_components(expected_max_difference=3E-3						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			[
      'DDIMScheduler',
      'DDPMScheduler',
      'PNDMScheduler',
      'HeunDiscreteScheduler',
      'EulerAncestralDiscreteScheduler',
      'KDPM2DiscreteScheduler',
      'KDPM2AncestralDiscreteScheduler',
      'DPMSolverSDEScheduler',
  ]
  __A  :		Any       =			self.get_dummy_components()
  __A  :		List[str]       =			self.pipeline_class(**__lowercase						)

  # make sure that PNDM does not need warm-up
  pipe.scheduler.register_to_config(skip_prk_steps=__lowercase						)

  pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)
  __A  :		Union[str, Any]       =			self.get_dummy_inputs(__lowercase						)
  __A  :		Tuple       =			2

  __A  :		List[Any]       =			[]
  for scheduler_enum in KarrasDiffusionSchedulers:
   if scheduler_enum.name in skip_schedulers:
    # no sigma schedulers are not supported
    # no schedulers
    continue

   __A  :		Dict       =			getattr(__lowercase      ,      scheduler_enum.name						)
   __A  :		List[str]       =			scheduler_cls.from_config(pipe.scheduler.config						)
   __A  :		str       =			pipe(**__lowercase						)[0]
   outputs.append(__lowercase						)

  assert check_same_shape(__lowercase						)


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




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			torch.manual_seed(33						)

  __A  :		str       =			StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'      ,      torch_dtype=torch.floataa						)
  pipe.to('cuda'						)

  __A  :		str       =			StableDiffusionLatentUpscalePipeline.from_pretrained(
      'stabilityai/sd-x2-latent-upscaler'      ,      torch_dtype=torch.floataa						)
  upscaler.to('cuda'						)

  __A  :		Optional[int]       =			'a photo of an astronaut high resolution, unreal engine, ultra realistic'

  __A  :		Optional[int]       =			pipe(__lowercase      ,      generator=__lowercase      ,      output_type='latent'						).images

  __A  :		int       =			upscaler(
      prompt=__lowercase      ,      image=__lowercase      ,      num_inference_steps=20      ,      guidance_scale=0      ,      generator=__lowercase      ,      output_type='np'      ,      ).images[0]

  __A  :		List[str]       =			load_numpy(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'						)
  assert np.abs((expected_image - image).mean()						) < 5E-2







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			torch.manual_seed(33						)

  __A  :		Tuple       =			StableDiffusionLatentUpscalePipeline.from_pretrained(
      'stabilityai/sd-x2-latent-upscaler'      ,      torch_dtype=torch.floataa						)
  upscaler.to('cuda'						)

  __A  :		Any       =			'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

  __A  :		str       =			load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'						)

  __A  :		Optional[int]       =			upscaler(
      prompt=__lowercase      ,      image=__lowercase      ,      num_inference_steps=20      ,      guidance_scale=0      ,      generator=__lowercase      ,      output_type='np'      ,      ).images[0]

  __A  :		Tuple       =			load_numpy(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'						)
  assert np.abs((expected_image - image).max()						) < 5E-2










'''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 			_lowercase       (				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Optional[int]			):
 # 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
 __A  :		Optional[int]       =			TapasConfig.from_json_file(UpperCamelCase__			)
 # set absolute/relative position embeddings parameter
 __A  :		List[str]       =			reset_position_index_per_cell

 # set remaining parameters of TapasConfig as well as the model based on the task
 if task == "SQA":
  __A  :		Optional[int]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "WTQ":
  # run_task_main.py hparams
  __A  :		List[str]       =			4
  __A  :		Any       =			True
  # hparam_utils.py hparams
  __A  :		Any       =			0.664694
  __A  :		Dict       =			0.207951
  __A  :		Optional[Any]       =			0.121194
  __A  :		Any       =			True
  __A  :		Any       =			True
  __A  :		int       =			False
  __A  :		Optional[int]       =			0.0352513

  __A  :		List[Any]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "WIKISQL_SUPERVISED":
  # run_task_main.py hparams
  __A  :		Tuple       =			4
  __A  :		Optional[int]       =			False
  # hparam_utils.py hparams
  __A  :		List[str]       =			36.4519
  __A  :		Union[str, Any]       =			0.903421
  __A  :		List[str]       =			222.088
  __A  :		Optional[Any]       =			True
  __A  :		Optional[int]       =			True
  __A  :		Union[str, Any]       =			True
  __A  :		str       =			0.763141

  __A  :		Union[str, Any]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "TABFACT":
  __A  :		Union[str, Any]       =			TapasForSequenceClassification(config=UpperCamelCase__			)
 elif task == "MLM":
  __A  :		Any       =			TapasForMaskedLM(config=UpperCamelCase__			)
 elif task == "INTERMEDIATE_PRETRAINING":
  __A  :		List[Any]       =			TapasModel(config=UpperCamelCase__			)
 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(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

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

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

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


if __name__ == "__main__":
 UpperCAmelCase_ :     Dict								=      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.'
 )
 UpperCAmelCase_ :     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,
 )






