


import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase					:       List[str]			    =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase					:       Union[str, Any]			    =     []
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}.cross_attn.out_proj.weight""",
            f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
        )
    )
    rename_keys.append(
        (
            f"""transformer.decoder.layers.{i}.cross_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"""))

    # q, k, v projections in self/cross-attention in decoder for conditional DETR
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", f"""decoder.layers.{i}.sa_qcontent_proj.weight""")
    )
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", f"""decoder.layers.{i}.sa_kcontent_proj.weight""")
    )
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", f"""decoder.layers.{i}.sa_qpos_proj.weight""")
    )
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", f"""decoder.layers.{i}.sa_kpos_proj.weight""")
    )
    rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.weight""", f"""decoder.layers.{i}.sa_v_proj.weight"""))
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", f"""decoder.layers.{i}.ca_qcontent_proj.weight""")
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", f"""decoder.layers.{i}.ca_kcontent_proj.weight""")
    )
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", f"""decoder.layers.{i}.ca_kpos_proj.weight""")
    )
    rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.weight""", f"""decoder.layers.{i}.ca_v_proj.weight"""))
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", f"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
    )

    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", f"""decoder.layers.{i}.sa_qcontent_proj.bias""")
    )
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", f"""decoder.layers.{i}.sa_kcontent_proj.bias""")
    )
    rename_keys.append((f"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", f"""decoder.layers.{i}.sa_qpos_proj.bias"""))
    rename_keys.append((f"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", f"""decoder.layers.{i}.sa_kpos_proj.bias"""))
    rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.bias""", f"""decoder.layers.{i}.sa_v_proj.bias"""))
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", f"""decoder.layers.{i}.ca_qcontent_proj.bias""")
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", f"""decoder.layers.{i}.ca_kcontent_proj.bias""")
    )
    rename_keys.append((f"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", f"""decoder.layers.{i}.ca_kpos_proj.bias"""))
    rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.bias""", f"""decoder.layers.{i}.ca_v_proj.bias"""))
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", f"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
    )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""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"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)



def UpperCamelCase_				(				__a			,				__a			,				__a      )	->   Any:
      a__	:  Union[str, Any]     =   state_dict.pop(__a      )
      a__	:  List[str]     =   val



def UpperCamelCase_				(				__a      )	->   List[str]:
      a__	:  Any     =   OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  a__	:  str     =   key.replace("backbone.0.body"			,				"backbone.conv_encoder.model"      )
                  a__	:  Tuple     =   value
            else:
                  a__	:  List[str]     =   value

      return new_state_dict



def UpperCamelCase_				(				__a			,				__a=False      )	->   Any:
      a__	:  Tuple     =   ""
      if is_panoptic:
            a__	:  List[Any]     =   "conditional_detr."

      # 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)
            a__	:  Optional[Any]     =   state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''      )
            a__	:  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
            a__	:  Tuple     =   in_proj_weight[:256, :]
            a__	:  Any     =   in_proj_bias[:256]
            a__	:  Tuple     =   in_proj_weight[256:512, :]
            a__	:  List[str]     =   in_proj_bias[256:512]
            a__	:  Any     =   in_proj_weight[-256:, :]
            a__	:  Optional[int]     =   in_proj_bias[-256:]



def UpperCamelCase_				(				)	->   Tuple:
      a__	:  Optional[Any]     =   "http://images.cocodataset.org/val2017/000000039769.jpg"
      a__	:  List[Any]     =   Image.open(requests.get(__a			,				stream=__a      ).raw      )

      return im



@torch.no_grad()
def UpperCamelCase_				(				__a			,				__a      )	->   Dict:
      a__	:  Tuple     =   ConditionalDetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
            a__	:  Tuple     =   "resnet101"
      if "dc5" in model_name:
            a__	:  str     =   True
      a__	:  int     =   "panoptic" in model_name
      if is_panoptic:
            a__	:  List[str]     =   250
      else:
            a__	:  str     =   91
            a__	:  Union[str, Any]     =   "huggingface/label-files"
            a__	:  int     =   "coco-detection-id2label.json"
            a__	:  Optional[Any]     =   json.load(open(hf_hub_download(__a			,				__a			,				repo_type="dataset"      )			,				"r"      )      )
            a__	:  Any     =   {int(__a      ): v for k, v in idalabel.items()}
            a__	:  Union[str, Any]     =   idalabel
            a__	:  Optional[Any]     =   {v: k for k, v in idalabel.items()}

      # load image processor
      a__	:  Dict     =   "coco_panoptic" if is_panoptic else "coco_detection"
      a__	:  int     =   ConditionalDetrImageProcessor(format=__a      )

      # prepare image
      a__	:  Any     =   prepare_img()
      a__	:  Dict     =   image_processor(images=__a			,				return_tensors="pt"      )
      a__	:  List[Any]     =   encoding["pixel_values"]

      logger.info(f'''Converting model {model_name}...'''      )

      # load original model from torch hub
      a__	:  Optional[Any]     =   torch.hub.load("DeppMeng/ConditionalDETR"			,				__a			,				pretrained=__a      ).eval()
      a__	:  List[str]     =   conditional_detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
            if is_panoptic:
                  a__	:  str     =   "conditional_detr." + src
            rename_key(__a			,				__a			,				__a      )
      a__	:  Tuple     =   rename_backbone_keys(__a      )
      # query, key and value matrices need special treatment
      read_in_q_k_v(__a			,				is_panoptic=__a      )
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      a__	:  str     =   "conditional_detr.model." if is_panoptic else "model."
      for key in state_dict.copy().keys():
            if is_panoptic:
                  if (
                      key.startswith("conditional_detr"      )
                      and not key.startswith("class_labels_classifier"      )
                      and not key.startswith("bbox_predictor"      )
                  ):
                        a__	:  Any     =   state_dict.pop(__a      )
                        a__	:  Optional[int]     =   val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        a__	:  Dict     =   state_dict.pop(__a      )
                        a__	:  Tuple     =   val
                  elif key.startswith("bbox_attention"      ) or key.startswith("mask_head"      ):
                        continue
                  else:
                        a__	:  Optional[Any]     =   state_dict.pop(__a      )
                        a__	:  Dict     =   val
            else:
                  if not key.startswith("class_labels_classifier"      ) and not key.startswith("bbox_predictor"      ):
                        a__	:  str     =   state_dict.pop(__a      )
                        a__	:  Union[str, Any]     =   val
    # finally, create HuggingFace model and load state dict
      a__	:  str     =   ConditionalDetrForSegmentation(__a      ) if is_panoptic else ConditionalDetrForObjectDetection(__a      )
      model.load_state_dict(__a      )
      model.eval()
      model.push_to_hub(repo_id=__a			,				organization="DepuMeng"			,				commit_message="Add model"      )
      # verify our conversion
      a__	:  Dict     =   conditional_detr(__a      )
      a__	:  int     =   model(__a      )
      assert torch.allclose(outputs.logits			,				original_outputs["pred_logits"]			,				atol=1e-4      )
      assert torch.allclose(outputs.pred_boxes			,				original_outputs["pred_boxes"]			,				atol=1e-4      )
      if is_panoptic:
            assert torch.allclose(outputs.pred_masks			,				original_outputs["pred_masks"]			,				atol=1e-4      )

      # 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 __name__ == "__main__":
    UpperCamelCase					:       Dict			    =     argparse.ArgumentParser()

    parser.add_argument(
        """--model_name""",
        default="""conditional_detr_resnet50""",
        type=str,
        help="""Name of the CONDITIONAL_DETR model 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."""
    )
    UpperCamelCase					:       Optional[int]			    =     parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









import gc
import unittest

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

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

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


enable_full_determinism()







class 		_A						( UpperCAmelCase_	,				unittest.TestCase ):
  lowercase_						:			Union[str, Any]						=					LDMTextToImagePipeline
  lowercase_						:			Tuple						=					TEXT_TO_IMAGE_PARAMS - {
      '''negative_prompt''',
      '''negative_prompt_embeds''',
      '''cross_attention_kwargs''',
      '''prompt_embeds''',
  }
  lowercase_						:			Optional[Any]						=					PipelineTesterMixin.required_optional_params - {
      '''num_images_per_prompt''',
      '''callback''',
      '''callback_steps''',
  }
  lowercase_						:			Dict						=					TEXT_TO_IMAGE_BATCH_PARAMS
  lowercase_						:			Tuple						=					False

  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     torch.manual_seed(0			)
     __UpperCamelCase	:			Any					=					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""")   ,				cross_attention_dim=32   ,				)
     __UpperCamelCase	:			Union[str, Any]					=					DDIMScheduler(
         beta_start=0.0_0085   ,				beta_end=0.012   ,				beta_schedule="""scaled_linear"""   ,				clip_sample=lowerCamelCase__   ,				set_alpha_to_one=lowerCamelCase__   ,				)
     torch.manual_seed(0			)
     __UpperCamelCase	:			Any					=					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   ,				)
     torch.manual_seed(0			)
     __UpperCamelCase	:			Union[str, Any]					=					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=10_00   ,				)
     __UpperCamelCase	:			List[str]					=					CLIPTextModel(lowerCamelCase__			)
     __UpperCamelCase	:			List[str]					=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

     __UpperCamelCase	:			Any					=					{
         """unet""": unet,
         """scheduler""": scheduler,
         """vqvae""": vae,
         """bert""": text_encoder,
         """tokenizer""": tokenizer,
     }
     return components

  def    a					(    self						:	Tuple   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	List[str]=0			):
     """simple docstring"""

     if str(lowerCamelCase__			).startswith("""mps"""			):
        __UpperCamelCase	:			str					=					torch.manual_seed(lowerCamelCase__			)
     else:
        __UpperCamelCase	:			Optional[Any]					=					torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					{
         """prompt""": """A painting of a squirrel eating a burger""",
         """generator""": generator,
         """num_inference_steps""": 2,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs

  def    a					(    self						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator

     __UpperCamelCase	:			List[str]					=					self.get_dummy_components()
     __UpperCamelCase	:			Optional[int]					=					LDMTextToImagePipeline(**lowerCamelCase__			)
     pipe.to(lowerCamelCase__			)
     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			List[Any]					=					self.get_dummy_inputs(lowerCamelCase__			)
     __UpperCamelCase	:			List[Any]					=					pipe(**lowerCamelCase__			).images
     __UpperCamelCase	:			Tuple					=					image[0, -3:, -3:, -1]

     assert image.shape == (1, 16, 16, 3)
     __UpperCamelCase	:			int					=					np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]			)

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







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

  def    a					(    self						:	Any			):
     """simple docstring"""

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

  def    a					(    self						:	Dict   ,				lowerCamelCase__						:	List[Any]   ,				lowerCamelCase__						:	Tuple=torch.floataa   ,				lowerCamelCase__						:	List[Any]=0			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					torch.manual_seed(lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					np.random.RandomState(lowerCamelCase__			).standard_normal((1, 4, 32, 32)			)
     __UpperCamelCase	:			Any					=					torch.from_numpy(lowerCamelCase__			).to(device=lowerCamelCase__   ,				dtype=lowerCamelCase__			)
     __UpperCamelCase	:			Optional[int]					=					{
         """prompt""": """A painting of a squirrel eating a burger""",
         """latents""": latents,
         """generator""": generator,
         """num_inference_steps""": 3,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs

  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""			).to(lowerCamelCase__			)
     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			Any					=					self.get_inputs(lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					pipe(**lowerCamelCase__			).images
     __UpperCamelCase	:			Optional[int]					=					image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 2_56, 2_56, 3)
     __UpperCamelCase	:			Optional[Any]					=					np.array([0.5_1825, 0.5_2850, 0.5_2543, 0.5_4258, 0.5_2304, 0.5_2569, 0.5_4363, 0.5_5276, 0.5_6878]			)
     __UpperCamelCase	:			Union[str, Any]					=					np.abs(expected_slice - image_slice			).max()
     assert max_diff < 1e-3







@nightly
@require_torch_gpu
class 		_A						( unittest.TestCase ):

  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

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

  def    a					(    self						:	Tuple   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Union[str, Any]=torch.floataa   ,				lowerCamelCase__						:	Tuple=0			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					torch.manual_seed(lowerCamelCase__			)
     __UpperCamelCase	:			Optional[Any]					=					np.random.RandomState(lowerCamelCase__			).standard_normal((1, 4, 32, 32)			)
     __UpperCamelCase	:			List[Any]					=					torch.from_numpy(lowerCamelCase__			).to(device=lowerCamelCase__   ,				dtype=lowerCamelCase__			)
     __UpperCamelCase	:			Union[str, Any]					=					{
         """prompt""": """A painting of a squirrel eating a burger""",
         """latents""": latents,
         """generator""": generator,
         """num_inference_steps""": 50,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs

  def    a					(    self						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""			).to(lowerCamelCase__			)
     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			Dict					=					self.get_inputs(lowerCamelCase__			)
     __UpperCamelCase	:			List[Any]					=					pipe(**lowerCamelCase__			).images[0]

     __UpperCamelCase	:			Optional[Any]					=					load_numpy(
         """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"""			)
     __UpperCamelCase	:			int					=					np.abs(expected_image - image			).max()
     assert max_diff < 1e-3



