


import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase  :		Optional[Any]		     =	logging.get_logger(__name__)

def 			__UpperCamelCase    (    _A    :				List[Any]				)    ->      int:




	"""simple docstring"""





	if "resnet-50" in model_name:
		lowerCAmelCase					:    List[Any]      							= ResNetConfig.from_pretrained('microsoft/resnet-50'				)
	elif "resnet-101" in model_name:
		lowerCAmelCase					:    List[str]      							= ResNetConfig.from_pretrained('microsoft/resnet-101'				)
	else:
		raise ValueError('Model name should include either resnet50 or resnet101'				)

	lowerCAmelCase					:    Optional[int]      							= DetrConfig(use_timm_backbone=__UpperCamelCase					,   backbone_config=__UpperCamelCase				)

	# set label attributes
	lowerCAmelCase					:    Optional[int]      							= 'panoptic' in model_name
	if is_panoptic:
		lowerCAmelCase					:    Union[str, Any]      							= 2_50
	else:
		lowerCAmelCase					:    Optional[int]      							= 91
		lowerCAmelCase					:    Optional[int]      							= 'huggingface/label-files'
		lowerCAmelCase					:    Optional[int]      							= 'coco-detection-id2label.json'
		lowerCAmelCase					:    int      							= json.load(open(hf_hub_download(__UpperCamelCase					,   __UpperCamelCase					,   repo_type='dataset'				)					,   'r'				)				)
		lowerCAmelCase					:    str      							= {int(__UpperCamelCase				): v for k, v in idalabel.items()}
		lowerCAmelCase					:    Union[str, Any]      							= idalabel
		lowerCAmelCase					:    List[Any]      							= {v: k for k, v in idalabel.items()}

	return config, is_panoptic

def 			__UpperCamelCase    (    _A    :				List[Any]				)    ->      List[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[Any]      							= []

	# stem
	# fmt: off
	rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')				)
	rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')				)
	rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')				)
	rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')				)
	rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')				)
	# stages
	for stage_idx in range(len(config.backbone_config.depths				)				):
		for layer_idx in range(config.backbone_config.depths[stage_idx]				):
			# shortcut
			if layer_idx == 0:
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
				    )				)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
				    )				)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
				    )				)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
				    )				)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
				    )				)
			# 3 convs
			for i in range(3				):
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
				    )				)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
				    )				)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
				    )				)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
				    )				)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
				    )				)
    # fmt: on

	for i in range(config.encoder_layers				):
		# 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 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.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'),
	    ]				)

	return rename_keys

def 			__UpperCamelCase    (    _A    :				str					,   _A    :				List[str]					,   _A    :				List[str]				)    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= state_dict.pop(__UpperCamelCase				)
	lowerCAmelCase					:    Tuple      							= val

def 			__UpperCamelCase    (    _A    :				Tuple					,   _A    :				int=False				)    ->      Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= ''
	if is_panoptic:
		lowerCAmelCase					:    Dict      							= '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					:    Tuple      							= state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"				)
		lowerCAmelCase					:    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
		lowerCAmelCase					:    List[str]      							= in_proj_weight[:2_56, :]
		lowerCAmelCase					:    Tuple      							= in_proj_bias[:2_56]
		lowerCAmelCase					:    Dict      							= in_proj_weight[2_56:5_12, :]
		lowerCAmelCase					:    List[Any]      							= in_proj_bias[2_56:5_12]
		lowerCAmelCase					:    Union[str, Any]      							= in_proj_weight[-2_56:, :]
		lowerCAmelCase					:    List[Any]      							= in_proj_bias[-2_56:]
	# 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
		lowerCAmelCase					:    List[str]      							= state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"				)
		lowerCAmelCase					:    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
		lowerCAmelCase					:    Dict      							= in_proj_weight[:2_56, :]
		lowerCAmelCase					:    Dict      							= in_proj_bias[:2_56]
		lowerCAmelCase					:    str      							= in_proj_weight[2_56:5_12, :]
		lowerCAmelCase					:    Tuple      							= in_proj_bias[2_56:5_12]
		lowerCAmelCase					:    Any      							= in_proj_weight[-2_56:, :]
		lowerCAmelCase					:    Union[str, Any]      							= in_proj_bias[-2_56:]
		# read in weights + bias of input projection layer of cross-attention
		lowerCAmelCase					:    Optional[Any]      							= state_dict.pop(
		    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"				)
		lowerCAmelCase					:    Optional[Any]      							= 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
		lowerCAmelCase					:    Union[str, Any]      							= in_proj_weight_cross_attn[:2_56, :]
		lowerCAmelCase					:    List[Any]      							= in_proj_bias_cross_attn[:2_56]
		lowerCAmelCase					:    Optional[int]      							= in_proj_weight_cross_attn[2_56:5_12, :]
		lowerCAmelCase					:    str      							= in_proj_bias_cross_attn[2_56:5_12]
		lowerCAmelCase					:    List[Any]      							= in_proj_weight_cross_attn[-2_56:, :]
		lowerCAmelCase					:    int      							= in_proj_bias_cross_attn[-2_56:]

def 			__UpperCamelCase    (    )    ->      Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    List[str]      							= 'http://images.cocodataset.org/val2017/000000039769.jpg'
	lowerCAmelCase					:    str      							= Image.open(requests.get(__UpperCamelCase					,   stream=__UpperCamelCase				).raw				)

	return im

@torch.no_grad()
def 			__UpperCamelCase    (    _A    :				List[Any]					,   _A    :				List[str]=None					,   _A    :				Any=False				)    ->      Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase							,   lowerCAmelCase					:    int      							= get_detr_config(__UpperCamelCase				)

	# load original model from torch hub
	lowerCAmelCase					:    Any      							= {
	    'detr-resnet-50': 'detr_resnet50',
	    'detr-resnet-101': 'detr_resnet101',
	}
	logger.info(F"Converting model {model_name}..."				)
	lowerCAmelCase					:    str      							= torch.hub.load('facebookresearch/detr'					,   model_name_to_original_name[model_name]					,   pretrained=__UpperCamelCase				).eval()
	lowerCAmelCase					:    Optional[Any]      							= detr.state_dict()
	# rename keys
	for src, dest in create_rename_keys(__UpperCamelCase				):
		if is_panoptic:
			lowerCAmelCase					:    Any      							= 'detr.' + src
		rename_key(__UpperCamelCase					,   __UpperCamelCase					,   __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					:    Dict      							= 'detr.model.' if is_panoptic else 'model.'
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith('detr'				)
			    and not key.startswith('class_labels_classifier'				)
			    and not key.startswith('bbox_predictor'				)
			):
				lowerCAmelCase					:    List[Any]      							= state_dict.pop(__UpperCamelCase				)
				lowerCAmelCase					:    Dict      							= val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				lowerCAmelCase					:    Optional[int]      							= state_dict.pop(__UpperCamelCase				)
				lowerCAmelCase					:    Dict      							= val
			elif key.startswith('bbox_attention'				) or key.startswith('mask_head'				):
				continue
			else:
				lowerCAmelCase					:    Optional[int]      							= state_dict.pop(__UpperCamelCase				)
				lowerCAmelCase					:    List[Any]      							= val
		else:
			if not key.startswith('class_labels_classifier'				) and not key.startswith('bbox_predictor'				):
				lowerCAmelCase					:    int      							= state_dict.pop(__UpperCamelCase				)
				lowerCAmelCase					:    List[Any]      							= val

    # finally, create HuggingFace model and load state dict
	lowerCAmelCase					:    Any      							= DetrForSegmentation(__UpperCamelCase				) if is_panoptic else DetrForObjectDetection(__UpperCamelCase				)
	model.load_state_dict(__UpperCamelCase				)
	model.eval()

	# verify our conversion on an image
	lowerCAmelCase					:    Any      							= 'coco_panoptic' if is_panoptic else 'coco_detection'
	lowerCAmelCase					:    Any      							= DetrImageProcessor(format=__UpperCamelCase				)

	lowerCAmelCase					:    Tuple      							= processor(images=prepare_img()					,   return_tensors='pt'				)
	lowerCAmelCase					:    List[str]      							= encoding['pixel_values']

	lowerCAmelCase					:    int      							= detr(__UpperCamelCase				)
	lowerCAmelCase					:    Any      							= model(__UpperCamelCase				)

	assert torch.allclose(outputs.logits					,   original_outputs['pred_logits']					,   atol=1e-3				)
	assert torch.allclose(outputs.pred_boxes					,   original_outputs['pred_boxes']					,   atol=1e-3				)
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks					,   original_outputs['pred_masks']					,   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(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)
		model.save_pretrained(__UpperCamelCase				)
		processor.save_pretrained(__UpperCamelCase				)

	if push_to_hub:
		# Upload model and image processor to the hub
		logger.info('Uploading PyTorch model and image processor to the hub...'				)
		model.push_to_hub(F"nielsr/{model_name}"				)
		processor.push_to_hub(F"nielsr/{model_name}"				)


if __name__ == "__main__":
				_lowerCAmelCase  :		Tuple		     =	argparse.ArgumentParser()

				parser.add_argument(
				    '--model_name',
				    default='detr-resnet-50',
				    type=str,
				    choices=['detr-resnet-50', 'detr-resnet-101'],
				    help='Name of the 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.'
				)
				parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
				_lowerCAmelCase  :		Union[str, Any]		     =	parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''




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  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					List[Any]			      = LDMTextToImagePipeline
	_lowerCamelCase   :					Optional[Any]			      = TEXT_TO_IMAGE_PARAMS - {
	    """negative_prompt""",
	    """negative_prompt_embeds""",
	    """cross_attention_kwargs""",
	    """prompt_embeds""",
	}
	_lowerCamelCase   :					List[str]			      = PipelineTesterMixin.required_optional_params - {
	    """num_images_per_prompt""",
	    """callback""",
	    """callback_steps""",
	}
	_lowerCamelCase   :					Union[str, Any]			      = TEXT_TO_IMAGE_BATCH_PARAMS
	_lowerCamelCase   :					Optional[int]			      = False



	def 				lowercase     (					self    ):
		torch.manual_seed(0    )
		lowerCAmelCase					:    Dict      							= 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 ,					)
		lowerCAmelCase					:    int      							= DDIMScheduler(
		    beta_start=0.0_0_0_8_5 ,					beta_end=0.0_1_2 ,					beta_schedule='scaled_linear' ,					clip_sample=snake_case__ ,					set_alpha_to_one=snake_case__ ,					)
		torch.manual_seed(0    )
		lowerCAmelCase					:    str      							= 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    )
		lowerCAmelCase					:    Any      							= CLIPTextConfig(
		    bos_token_id=0 ,					eos_token_id=2 ,					hidden_size=32 ,					intermediate_size=37 ,					layer_norm_eps=1e-0_5 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					pad_token_id=1 ,					vocab_size=1000 ,					)
		lowerCAmelCase					:    str      							= CLIPTextModel(snake_case__    )
		lowerCAmelCase					:    str      							= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

		lowerCAmelCase					:    List[Any]      							= {
		    'unet': unet,
		    'scheduler': scheduler,
		    'vqvae': vae,
		    'bert': text_encoder,
		    'tokenizer': tokenizer,
		}
		return components



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=0    ):
		if str(snake_case__    ).startswith('mps'    ):
			lowerCAmelCase					:    Optional[int]      							= torch.manual_seed(snake_case__    )
		else:
			lowerCAmelCase					:    str      							= torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
		lowerCAmelCase					:    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 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= 'cpu'  # ensure determinism for the device-dependent torch.Generator

		lowerCAmelCase					:    Optional[Any]      							= self.get_dummy_components()
		lowerCAmelCase					:    Optional[Any]      							= LDMTextToImagePipeline(**snake_case__    )
		pipe.to(snake_case__    )
		pipe.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    Tuple      							= self.get_dummy_inputs(snake_case__    )
		lowerCAmelCase					:    Union[str, Any]      							= pipe(**snake_case__    ).images
		lowerCAmelCase					:    str      							= image[0, -3:, -3:, -1]

		assert image.shape == (1, 16, 16, 3)
		lowerCAmelCase					:    List[Any]      							= np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4]    )

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



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



	def 				lowercase     (					self    ):
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=torch.floataa ,					snake_case__=0    ):
		lowerCAmelCase					:    List[str]      							= torch.manual_seed(snake_case__    )
		lowerCAmelCase					:    int      							= np.random.RandomState(snake_case__    ).standard_normal((1, 4, 32, 32)    )
		lowerCAmelCase					:    Optional[Any]      							= torch.from_numpy(snake_case__    ).to(device=snake_case__ ,					dtype=snake_case__    )
		lowerCAmelCase					:    List[str]      							= {
		    '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 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'    ).to(snake_case__    )
		pipe.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    Optional[Any]      							= self.get_inputs(snake_case__    )
		lowerCAmelCase					:    List[Any]      							= pipe(**snake_case__    ).images
		lowerCAmelCase					:    str      							= image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 256, 256, 3)
		lowerCAmelCase					:    Tuple      							= np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8]    )
		lowerCAmelCase					:    int      							= np.abs(expected_slice - image_slice    ).max()
		assert max_diff < 1e-3



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



	def 				lowercase     (					self    ):
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=torch.floataa ,					snake_case__=0    ):
		lowerCAmelCase					:    List[str]      							= torch.manual_seed(snake_case__    )
		lowerCAmelCase					:    Any      							= np.random.RandomState(snake_case__    ).standard_normal((1, 4, 32, 32)    )
		lowerCAmelCase					:    List[Any]      							= torch.from_numpy(snake_case__    ).to(device=snake_case__ ,					dtype=snake_case__    )
		lowerCAmelCase					:    List[str]      							= {
		    '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 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'    ).to(snake_case__    )
		pipe.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    int      							= self.get_inputs(snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= pipe(**snake_case__    ).images[0]

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