






import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE 							=						[]
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
  )
  rename_keys.append(
      (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
  )
  rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
  rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
  rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
  rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
  rename_keys.append(
      (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
  )
  rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
  rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
  rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
  )
  rename_keys.append(
      (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
  )
  rename_keys.append(
      (
          F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
          F'decoder.layers.{i}.encoder_attn.out_proj.weight',
      )
  )
  rename_keys.append(
      (
          F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
          F'decoder.layers.{i}.encoder_attn.out_proj.bias',
      )
  )
  rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
  rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
  rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
  rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
  rename_keys.append(
      (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
  )
  rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
  rename_keys.append(
      (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
  )
  rename_keys.append(
      (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
  )
  rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
  rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
        ('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
    ]
)

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							Tuple		,		__A    :							List[str] )      ->					Optional[Any]:
    _UpperCAmelCase				:      List[Any]			  =				state_dict.pop(__A )
    _UpperCAmelCase				:      List[Any]			  =				val

def      _lowerCamelCase	(						__A    :							int )      ->					str:
    _UpperCAmelCase				:      int			  =				OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            _UpperCAmelCase				:      Optional[Any]			  =				key.replace('''backbone.0.body'''		,		'''backbone.conv_encoder.model''' )
            _UpperCAmelCase				:      int			  =				value
        else:
            _UpperCAmelCase				:      str			  =				value

    return new_state_dict

def      _lowerCamelCase	(						__A    :							int )      ->					Union[str, Any]:
    _UpperCAmelCase				:      Any			  =				''''''

    # first: transformer encoder
    for i in range(6 ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        _UpperCAmelCase				:      List[Any]			  =				state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
        _UpperCAmelCase				:      Any			  =				state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase				:      Union[str, Any]			  =				in_proj_weight[:256, :]
        _UpperCAmelCase				:      List[Any]			  =				in_proj_bias[:256]
        _UpperCAmelCase				:      Dict			  =				in_proj_weight[256:512, :]
        _UpperCAmelCase				:      Dict			  =				in_proj_bias[256:512]
        _UpperCAmelCase				:      List[Any]			  =				in_proj_weight[-256:, :]
        _UpperCAmelCase				:      Optional[Any]			  =				in_proj_bias[-256:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6 ):
        # read in weights + bias of input projection layer of self-attention
        _UpperCAmelCase				:      Any			  =				state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
        _UpperCAmelCase				:      Union[str, Any]			  =				state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase				:      Tuple			  =				in_proj_weight[:256, :]
        _UpperCAmelCase				:      Union[str, Any]			  =				in_proj_bias[:256]
        _UpperCAmelCase				:      List[Any]			  =				in_proj_weight[256:512, :]
        _UpperCAmelCase				:      Any			  =				in_proj_bias[256:512]
        _UpperCAmelCase				:      Optional[Any]			  =				in_proj_weight[-256:, :]
        _UpperCAmelCase				:      Dict			  =				in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        _UpperCAmelCase				:      List[Any]			  =				state_dict.pop(
            f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
        _UpperCAmelCase				:      Dict			  =				state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        _UpperCAmelCase				:      Union[str, Any]			  =				in_proj_weight_cross_attn[:256, :]
        _UpperCAmelCase				:      str			  =				in_proj_bias_cross_attn[:256]
        _UpperCAmelCase				:      List[str]			  =				in_proj_weight_cross_attn[256:512, :]
        _UpperCAmelCase				:      Optional[Any]			  =				in_proj_bias_cross_attn[256:512]
        _UpperCAmelCase				:      List[Any]			  =				in_proj_weight_cross_attn[-256:, :]
        _UpperCAmelCase				:      List[str]			  =				in_proj_bias_cross_attn[-256:]

def      _lowerCamelCase	(						__A    :							Optional[Any]		,		__A    :							Optional[Any] )      ->					Tuple:
    _UpperCAmelCase  , _UpperCAmelCase				:      int			  =				image.size
    _UpperCAmelCase				:      str			  =				max(__A		,		__A )
    _UpperCAmelCase				:      List[Any]			  =				800 if '''detection''' in checkpoint_url else 1_000
    _UpperCAmelCase				:      Dict			  =				target_max_size / current_max_size
    _UpperCAmelCase				:      List[str]			  =				image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )

    return resized_image

def      _lowerCamelCase	(						__A    :							List[str] )      ->					Union[str, Any]:
    _UpperCAmelCase				:      Union[str, Any]			  =				F.to_tensor(__A )
    _UpperCAmelCase				:      Dict			  =				F.normalize(__A		,		mean=[0.485, 0.456, 0.406]		,		std=[0.229, 0.224, 0.225] )
    return image

@torch.no_grad()
def      _lowerCamelCase	(						__A    :							Union[str, Any]		,		__A    :							int		,		__A    :							Dict )      ->					int:
    logger.info('''Converting model...''' )

    # load original state dict
    _UpperCAmelCase				:      str			  =				torch.hub.load_state_dict_from_url(__A		,		map_location='''cpu''' )
    # rename keys
    for src, dest in rename_keys:
        rename_key(__A		,		__A		,		__A )
    _UpperCAmelCase				:      Any			  =				rename_backbone_keys(__A )
    # query, key and value matrices need special treatment
    read_in_q_k_v(__A )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    _UpperCAmelCase				:      List[Any]			  =				'''model.'''
    for key in state_dict.copy().keys():
        if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
            _UpperCAmelCase				:      Tuple			  =				state_dict.pop(__A )
            _UpperCAmelCase				:      Optional[int]			  =				val
    # create HuggingFace model and load state dict
    _UpperCAmelCase				:      Optional[Any]			  =				TableTransformerConfig(
        backbone='''resnet18'''		,		mask_loss_coefficient=1		,		dice_loss_coefficient=1		,		ce_loss_coefficient=1		,		bbox_loss_coefficient=5		,		giou_loss_coefficient=2		,		eos_coefficient=0.4		,		class_cost=1		,		bbox_cost=5		,		giou_cost=2		,		)

    if "detection" in checkpoint_url:
        _UpperCAmelCase				:      List[Any]			  =				15
        _UpperCAmelCase				:      Tuple			  =				2
        _UpperCAmelCase				:      Tuple			  =				{0: '''table''', 1: '''table rotated'''}
        _UpperCAmelCase				:      str			  =				idalabel
        _UpperCAmelCase				:      Tuple			  =				{v: k for k, v in idalabel.items()}
    else:
        _UpperCAmelCase				:      Optional[Any]			  =				125
        _UpperCAmelCase				:      List[str]			  =				6
        _UpperCAmelCase				:      List[Any]			  =				{
            0: '''table''',
            1: '''table column''',
            2: '''table row''',
            3: '''table column header''',
            4: '''table projected row header''',
            5: '''table spanning cell''',
        }
        _UpperCAmelCase				:      Tuple			  =				idalabel
        _UpperCAmelCase				:      Optional[int]			  =				{v: k for k, v in idalabel.items()}

    _UpperCAmelCase				:      Optional[int]			  =				DetrImageProcessor(
        format='''coco_detection'''		,		max_size=800 if '''detection''' in checkpoint_url else 1_000 )
    _UpperCAmelCase				:      str			  =				TableTransformerForObjectDetection(__A )
    model.load_state_dict(__A )
    model.eval()

    # verify our conversion
    _UpperCAmelCase				:      str			  =				'''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
    _UpperCAmelCase				:      Optional[Any]			  =				hf_hub_download(repo_id='''nielsr/example-pdf'''		,		repo_type='''dataset'''		,		filename=__A )
    _UpperCAmelCase				:      Optional[int]			  =				Image.open(__A ).convert('''RGB''' )
    _UpperCAmelCase				:      Tuple			  =				normalize(resize(__A		,		__A ) ).unsqueeze(0 )

    _UpperCAmelCase				:      Optional[Any]			  =				model(__A )

    if "detection" in checkpoint_url:
        _UpperCAmelCase				:      Union[str, Any]			  =				(1, 15, 3)
        _UpperCAmelCase				:      Optional[int]			  =				torch.tensor(
            [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
        _UpperCAmelCase				:      Optional[Any]			  =				torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )

    else:
        _UpperCAmelCase				:      str			  =				(1, 125, 7)
        _UpperCAmelCase				:      int			  =				torch.tensor(
            [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
        _UpperCAmelCase				:      List[str]			  =				torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )

    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, :3, :3]		,		__A		,		atol=1e-4 )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,		__A		,		atol=1e-4 )
    print('''Looks ok!''' )

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
        Path(__A ).mkdir(exist_ok=__A )
        model.save_pretrained(__A )
        image_processor.save_pretrained(__A )

    if push_to_hub:
        # Push model to HF hub
        logger.info('''Pushing model to the hub...''' )
        _UpperCAmelCase				:      str			  =				(
            '''microsoft/table-transformer-detection'''
            if '''detection''' in checkpoint_url
            else '''microsoft/table-transformer-structure-recognition'''
        )
        model.push_to_hub(__A )
        image_processor.push_to_hub(__A )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()

  parser.add_argument(
      '--checkpoint_url',
      default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
      type=str,
      choices=[
          'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
          'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
      ],
      help='URL of the Table Transformer checkpoint you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
  )
  parser.add_argument(
      '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 							A_    (						__lowercase ,   __lowercase ,   unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Dict =					StableDiffusionXLImgaImgPipeline
 _SCREAMING_SNAKE_CASE							:			Any =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
 _SCREAMING_SNAKE_CASE							:			List[Any] =					PipelineTesterMixin.required_optional_params - {"latents"}
 _SCREAMING_SNAKE_CASE							:			Dict =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 _SCREAMING_SNAKE_CASE							:			List[Any] =					IMAGE_TO_IMAGE_IMAGE_PARAMS
 _SCREAMING_SNAKE_CASE							:			Dict =					IMAGE_TO_IMAGE_IMAGE_PARAMS


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     torch.manual_seed(0)
     _UpperCAmelCase				:      Tuple			  =				UNetaDConditionModel(
         block_out_channels=(32, 64)							,  layers_per_block=2							,  sample_size=32							,  in_channels=4							,  out_channels=4							,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							,  attention_head_dim=(2, 4)							,  use_linear_projection=_A							,  addition_embed_type='''text_time'''							,  addition_time_embed_dim=8							,  transformer_layers_per_block=(1, 2)							,  projection_class_embeddings_input_dim=80							,  cross_attention_dim=64							,  )
     _UpperCAmelCase				:      int			  =				EulerDiscreteScheduler(
         beta_start=0.00085							,  beta_end=0.012							,  steps_offset=1							,  beta_schedule='''scaled_linear'''							,  timestep_spacing='''leading'''							,  )
     torch.manual_seed(0)
     _UpperCAmelCase				:      Optional[int]			  =				AutoencoderKL(
         block_out_channels=[32, 64]							,  in_channels=3							,  out_channels=3							,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']							,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							,  latent_channels=4							,  sample_size=128							,  )
     torch.manual_seed(0)
     _UpperCAmelCase				:      Tuple			  =				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=1000							,  hidden_act='''gelu'''							,  projection_dim=32							,  )
     _UpperCAmelCase				:      int			  =				CLIPTextModel(_A)
     _UpperCAmelCase				:      Tuple			  =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,  local_files_only=_A)

     _UpperCAmelCase				:      int			  =				CLIPTextModelWithProjection(_A)
     _UpperCAmelCase				:      Optional[int]			  =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,  local_files_only=_A)

     _UpperCAmelCase				:      Any			  =				{
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''vae''': vae,
         '''text_encoder''': text_encoder,
         '''tokenizer''': tokenizer,
         '''text_encoder_2''': text_encoder_a,
         '''tokenizer_2''': tokenizer_a,
         # "safety_checker": None,
         # "feature_extractor": None,
     }
     return components


 def  snake_case__				(	self							,  _A							,  _A=0) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				floats_tensor((1, 3, 32, 32)							,  rng=random.Random(_A)).to(_A)
     _UpperCAmelCase				:      List[Any]			  =				image / 2 + 0.5
     if str(_A).startswith('''mps'''):
         _UpperCAmelCase				:      Union[str, Any]			  =				torch.manual_seed(_A)
     else:
         _UpperCAmelCase				:      List[str]			  =				torch.Generator(device=_A).manual_seed(_A)
     _UpperCAmelCase				:      Tuple			  =				{
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''guidance_scale''': 5.0,
         '''output_type''': '''numpy''',
         '''strength''': 0.75,
     }
     return inputs


 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
     _UpperCAmelCase				:      Optional[Any]			  =				self.get_dummy_components()
     _UpperCAmelCase				:      Any			  =				StableDiffusionXLImgaImgPipeline(**_A)
     _UpperCAmelCase				:      List[str]			  =				sd_pipe.to(_A)
     sd_pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      Tuple			  =				self.get_dummy_inputs(_A)
     _UpperCAmelCase				:      Any			  =				sd_pipe(**_A).images
     _UpperCAmelCase				:      Any			  =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)

     _UpperCAmelCase				:      Optional[Any]			  =				np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165])

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


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

     """simple docstring"""



     super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)


 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



     super().test_inference_batch_single_identical(expected_max_diff=3e-3)


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

     """simple docstring"""



     pass


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

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				self.get_dummy_components()
     _UpperCAmelCase				:      str			  =				StableDiffusionXLImgaImgPipeline(**_A)
     _UpperCAmelCase				:      Any			  =				sd_pipe.to(_A)
     _UpperCAmelCase				:      Tuple			  =				sd_pipe.to(_A)
     sd_pipe.set_progress_bar_config(disable=_A)

     # forward without prompt embeds
     _UpperCAmelCase				:      str			  =				self.get_dummy_inputs(_A)
     _UpperCAmelCase				:      Optional[Any]			  =				3 * ['''this is a negative prompt''']
     _UpperCAmelCase				:      Optional[int]			  =				negative_prompt
     _UpperCAmelCase				:      Optional[int]			  =				3 * [inputs['''prompt''']]

     _UpperCAmelCase				:      Optional[Any]			  =				sd_pipe(**_A)
     _UpperCAmelCase				:      Any			  =				output.images[0, -3:, -3:, -1]

     # forward with prompt embeds
     _UpperCAmelCase				:      Dict			  =				self.get_dummy_inputs(_A)
     _UpperCAmelCase				:      Optional[Any]			  =				3 * ['''this is a negative prompt''']
     _UpperCAmelCase				:      Dict			  =				3 * [inputs.pop('''prompt''')]

     (
         (
         _UpperCAmelCase
     )  , (
         _UpperCAmelCase
     )  , (
         _UpperCAmelCase
     )  , (
         _UpperCAmelCase
     )  , 
     )				:      str			  =				sd_pipe.encode_prompt(_A							,  negative_prompt=_A)

     _UpperCAmelCase				:      str			  =				sd_pipe(
         **_A							,  prompt_embeds=_A							,  negative_prompt_embeds=_A							,  pooled_prompt_embeds=_A							,  negative_pooled_prompt_embeds=_A							,  )
     _UpperCAmelCase				:      List[str]			  =				output.images[0, -3:, -3:, -1]

     # make sure that it's equal
     assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()).max() < 1e-4



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






 '''simple docstring'''




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

     """simple docstring"""



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


 def  snake_case__				(	self							,  _A							,  _A="cpu"							,  _A=torch.floataa							,  _A=0) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				torch.Generator(device=_A).manual_seed(_A)
     _UpperCAmelCase				:      Any			  =				np.random.RandomState(_A).standard_normal((1, 4, 64, 64))
     _UpperCAmelCase				:      Dict			  =				torch.from_numpy(_A).to(device=_A							,  dtype=_A)
     _UpperCAmelCase				:      str			  =				{
         '''prompt''': '''a photograph of an astronaut riding a horse''',
         '''latents''': latents,
         '''generator''': generator,
         '''num_inference_steps''': 3,
         '''guidance_scale''': 7.5,
         '''output_type''': '''numpy''',
     }
     return inputs


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

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''')
     pipe.to(_A)
     pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      str			  =				self.get_inputs(_A)
     _UpperCAmelCase				:      Union[str, Any]			  =				pipe(**_A).images
     _UpperCAmelCase				:      Optional[int]			  =				image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 512, 512, 3)
     _UpperCAmelCase				:      int			  =				np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506])
     assert np.abs(image_slice - expected_slice).max() < 7e-3




