






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()
_a    :   Dict        =			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_a    :   Optional[int]        =			[]
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 				snake_case__							(							UpperCAmelCase							:  int    ,			UpperCAmelCase							:  List[str]    ,			UpperCAmelCase							:  str       ):
    lowerCAmelCase__   :Dict      							=				state_dict.pop(UpperCAmelCase       )
    lowerCAmelCase__   :Optional[Any]      							=				val


def 				snake_case__							(							UpperCAmelCase							:  List[Any]       ):
    lowerCAmelCase__   :str      							=				OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            lowerCAmelCase__   :int      							=				key.replace("backbone.0.body"    ,			"backbone.conv_encoder.model"       )
            lowerCAmelCase__   :Tuple      							=				value
        else:
            lowerCAmelCase__   :Optional[Any]      							=				value

    return new_state_dict


def 				snake_case__							(							UpperCAmelCase							:  Dict    ,			UpperCAmelCase							:  Tuple=False       ):
    lowerCAmelCase__   :str      							=				""
    if is_panoptic:
        lowerCAmelCase__   :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)
        lowerCAmelCase__   :Dict      							=				state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''       )
        lowerCAmelCase__   :str      							=				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
        lowerCAmelCase__   :Dict      							=				in_proj_weight[:2_5_6, :]
        lowerCAmelCase__   :List[Any]      							=				in_proj_bias[:2_5_6]
        lowerCAmelCase__   :Union[str, Any]      							=				in_proj_weight[2_5_6:5_1_2, :]
        lowerCAmelCase__   :List[Any]      							=				in_proj_bias[2_5_6:5_1_2]
        lowerCAmelCase__   :Dict      							=				in_proj_weight[-2_5_6:, :]
        lowerCAmelCase__   :Optional[Any]      							=				in_proj_bias[-2_5_6:]


def 				snake_case__							(							):
    lowerCAmelCase__   :Optional[int]      							=				"http://images.cocodataset.org/val2017/000000039769.jpg"
    lowerCAmelCase__   :List[Any]      							=				Image.open(requests.get(UpperCAmelCase    ,			stream=UpperCAmelCase       ).raw       )

    return im


@torch.no_grad()
def 				snake_case__							(							UpperCAmelCase							:  Union[str, Any]    ,			UpperCAmelCase							:  Union[str, Any]       ):
    lowerCAmelCase__   :str      							=				ConditionalDetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        lowerCAmelCase__   :List[str]      							=				"resnet101"
    if "dc5" in model_name:
        lowerCAmelCase__   :Optional[Any]      							=				True
    lowerCAmelCase__   :List[str]      							=				"panoptic" in model_name
    if is_panoptic:
        lowerCAmelCase__   :int      							=				2_5_0
    else:
        lowerCAmelCase__   :Union[str, Any]      							=				9_1
        lowerCAmelCase__   :Tuple      							=				"huggingface/label-files"
        lowerCAmelCase__   :int      							=				"coco-detection-id2label.json"
        lowerCAmelCase__   :Union[str, Any]      							=				json.load(open(hf_hub_download(UpperCAmelCase    ,			UpperCAmelCase    ,			repo_type="dataset"       )    ,			"r"       )       )
        lowerCAmelCase__   :Optional[Any]      							=				{int(UpperCAmelCase       ): v for k, v in idalabel.items()}
        lowerCAmelCase__   :int      							=				idalabel
        lowerCAmelCase__   :int      							=				{v: k for k, v in idalabel.items()}

    # load image processor
    lowerCAmelCase__   :Dict      							=				"coco_panoptic" if is_panoptic else "coco_detection"
    lowerCAmelCase__   :List[Any]      							=				ConditionalDetrImageProcessor(format=UpperCAmelCase       )

    # prepare image
    lowerCAmelCase__   :Any      							=				prepare_img()
    lowerCAmelCase__   :Dict      							=				image_processor(images=UpperCAmelCase    ,			return_tensors="pt"       )
    lowerCAmelCase__   :Optional[int]      							=				encoding["pixel_values"]

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

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


if __name__ == "__main__":
 _a    :   List[str]        =			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."""
 )
 _a    :   Union[str, Any]        =			parser.parse_args()
 convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)











import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference







class 	_UpperCAmelCase  (  _A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    ShapEImgaImgPipeline
      A          =    ['''image''']
      A          =    ['''image''']
      A          =    [
          '''num_images_per_prompt''',
          '''num_inference_steps''',
          '''generator''',
          '''latents''',
          '''guidance_scale''',
          '''frame_size''',
          '''output_type''',
          '''return_dict''',
      ]
      A          =    False
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 32
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 32
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self.time_input_dim * 4
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 8
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          torch.manual_seed(0		)
          lowerCAmelCase__   :Any      							=				CLIPVisionConfig(
              hidden_size=self.text_embedder_hidden_size    ,	image_size=64    ,	projection_dim=self.text_embedder_hidden_size    ,	intermediate_size=37    ,	num_attention_heads=4    ,	num_channels=3    ,	num_hidden_layers=5    ,	patch_size=1    ,	)

          lowerCAmelCase__   :int      							=				CLIPVisionModel(_lowerCAmelCase		)
          return model
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				CLIPImageProcessor(
              crop_size=224    ,	do_center_crop=_lowerCAmelCase    ,	do_normalize=_lowerCAmelCase    ,	do_resize=_lowerCAmelCase    ,	image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]    ,	image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]    ,	resample=3    ,	size=224    ,	)

          return image_processor
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          torch.manual_seed(0		)

          lowerCAmelCase__   :Any      							=				{
              "num_attention_heads": 2,
              "attention_head_dim": 16,
              "embedding_dim": self.time_input_dim,
              "num_embeddings": 32,
              "embedding_proj_dim": self.text_embedder_hidden_size,
              "time_embed_dim": self.time_embed_dim,
              "num_layers": 1,
              "clip_embed_dim": self.time_input_dim * 2,
              "additional_embeddings": 0,
              "time_embed_act_fn": "gelu",
              "norm_in_type": "layer",
              "embedding_proj_norm_type": "layer",
              "encoder_hid_proj_type": None,
              "added_emb_type": None,
          }

          lowerCAmelCase__   :int      							=				PriorTransformer(**_lowerCAmelCase		)
          return model
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          torch.manual_seed(0		)

          lowerCAmelCase__   :List[Any]      							=				{
              "param_shapes": (
                  (self.renderer_dim, 93),
                  (self.renderer_dim, 8),
                  (self.renderer_dim, 8),
                  (self.renderer_dim, 8),
              ),
              "d_latent": self.time_input_dim,
              "d_hidden": self.renderer_dim,
              "n_output": 12,
              "background": (
                  0.1,
                  0.1,
                  0.1,
              ),
          }
          lowerCAmelCase__   :str      							=				ShapERenderer(**_lowerCAmelCase		)
          return model
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				self.dummy_prior
          lowerCAmelCase__   :str      							=				self.dummy_image_encoder
          lowerCAmelCase__   :Optional[Any]      							=				self.dummy_image_processor
          lowerCAmelCase__   :Union[str, Any]      							=				self.dummy_renderer

          lowerCAmelCase__   :str      							=				HeunDiscreteScheduler(
              beta_schedule="exp"    ,	num_train_timesteps=1_024    ,	prediction_type="sample"    ,	use_karras_sigmas=_lowerCAmelCase    ,	clip_sample=_lowerCAmelCase    ,	clip_sample_range=1.0    ,	)
          lowerCAmelCase__   :Any      							=				{
              "prior": prior,
              "image_encoder": image_encoder,
              "image_processor": image_processor,
              "renderer": renderer,
              "scheduler": scheduler,
          }

          return components
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=0		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(_lowerCAmelCase		)		).to(_lowerCAmelCase		)

          if str(_lowerCAmelCase		).startswith("mps"		):
              lowerCAmelCase__   :Dict      							=				torch.manual_seed(_lowerCAmelCase		)
          else:
              lowerCAmelCase__   :str      							=				torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
          lowerCAmelCase__   :Tuple      							=				{
              "image": input_image,
              "generator": generator,
              "num_inference_steps": 1,
              "frame_size": 32,
              "output_type": "np",
          }
          return inputs
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				"cpu"

          lowerCAmelCase__   :Union[str, Any]      							=				self.get_dummy_components()

          lowerCAmelCase__   :Union[str, Any]      							=				self.pipeline_class(**_lowerCAmelCase		)
          lowerCAmelCase__   :Optional[int]      							=				pipe.to(_lowerCAmelCase		)

          pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

          lowerCAmelCase__   :int      							=				pipe(**self.get_dummy_inputs(_lowerCAmelCase		)		)
          lowerCAmelCase__   :List[Any]      							=				output.images[0]
          lowerCAmelCase__   :List[Any]      							=				image[0, -3:, -3:, -1]

          assert image.shape == (20, 32, 32, 3)

          lowerCAmelCase__   :str      							=				np.array(
              [
                  0.0003_9216,
                  0.0003_9216,
                  0.0003_9216,
                  0.0003_9216,
                  0.0003_9216,
                  0.0003_9216,
                  0.0003_9216,
                  0.0003_9216,
                  0.0003_9216,
              ]		)

          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
          self._test_inference_batch_consistent(batch_sizes=[1, 2]		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				torch_device == "cpu"
          lowerCAmelCase__   :Tuple      							=				True
          self._test_inference_batch_single_identical(
              batch_size=2    ,	test_max_difference=_lowerCAmelCase    ,	relax_max_difference=_lowerCAmelCase    ,	)



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				self.get_dummy_components()
          lowerCAmelCase__   :Tuple      							=				self.pipeline_class(**_lowerCAmelCase		)
          lowerCAmelCase__   :Optional[Any]      							=				pipe.to(_lowerCAmelCase		)
          pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

          lowerCAmelCase__   :Tuple      							=				1
          lowerCAmelCase__   :List[Any]      							=				2

          lowerCAmelCase__   :List[str]      							=				self.get_dummy_inputs(_lowerCAmelCase		)

          for key in inputs.keys():
              if key in self.batch_params:
                  lowerCAmelCase__   :Any      							=				batch_size * [inputs[key]]

          lowerCAmelCase__   :Optional[Any]      							=				pipe(**_lowerCAmelCase    ,	num_images_per_prompt=_lowerCAmelCase		)[0]

          assert images.shape[0] == batch_size * num_images_per_prompt







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




      """simple docstring"""



      def 			snake_case_      (   self		):



          '''simple docstring'''
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png"		)
          lowerCAmelCase__   :int      							=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/shap_e/test_shap_e_img2img_out.npy"		)
          lowerCAmelCase__   :Optional[int]      							=				ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img"		)
          lowerCAmelCase__   :Tuple      							=				pipe.to(_lowerCAmelCase		)
          pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

          lowerCAmelCase__   :Any      							=				torch.Generator(device=_lowerCAmelCase		).manual_seed(0		)

          lowerCAmelCase__   :List[Any]      							=				pipe(
              _lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	guidance_scale=3.0    ,	num_inference_steps=64    ,	frame_size=64    ,	output_type="np"    ,	).images[0]

          assert images.shape == (20, 64, 64, 3)

          assert_mean_pixel_difference(_lowerCAmelCase    ,	_lowerCAmelCase		)



