




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


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







def 			lowerCAmelCase  (     UpperCAmelCase	)  ->Dict:






					"""simple docstring"""






					if "backbone.0.body" in orig_key:
										__magic_name__ :							Tuple		       =							orig_key.replace('''backbone.0.body''',     '''backbone.conv_encoder.model'''	)
					if "transformer" in orig_key:
										__magic_name__ :							int		       =							orig_key.replace('''transformer.''',     ''''''	)
					if "norm1" in orig_key:
										if "encoder" in orig_key:
															__magic_name__ :							Union[str, Any]		       =							orig_key.replace('''norm1''',     '''self_attn_layer_norm'''	)
										else:
															__magic_name__ :							Dict		       =							orig_key.replace('''norm1''',     '''encoder_attn_layer_norm'''	)
					if "norm2" in orig_key:
										if "encoder" in orig_key:
															__magic_name__ :							Optional[Any]		       =							orig_key.replace('''norm2''',     '''final_layer_norm'''	)
										else:
															__magic_name__ :							Optional[Any]		       =							orig_key.replace('''norm2''',     '''self_attn_layer_norm'''	)
					if "norm3" in orig_key:
										__magic_name__ :							List[Any]		       =							orig_key.replace('''norm3''',     '''final_layer_norm'''	)
					if "linear1" in orig_key:
										__magic_name__ :							Union[str, Any]		       =							orig_key.replace('''linear1''',     '''fc1'''	)
					if "linear2" in orig_key:
										__magic_name__ :							List[Any]		       =							orig_key.replace('''linear2''',     '''fc2'''	)
					if "query_embed" in orig_key:
										__magic_name__ :							str		       =							orig_key.replace('''query_embed''',     '''query_position_embeddings'''	)
					if "cross_attn" in orig_key:
										__magic_name__ :							int		       =							orig_key.replace('''cross_attn''',     '''encoder_attn'''	)

					return orig_key







def 			lowerCAmelCase  (     UpperCAmelCase	)  ->str:






					"""simple docstring"""






					for i in range(6	):
										# read in weights + bias of input projection layer of self-attention
										__magic_name__ :							str		       =							state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''	)
										__magic_name__ :							List[str]		       =							state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_bias'''	)
										# next, add query, keys and values (in that order) to the state dict
										__magic_name__ :							Optional[Any]		       =							in_proj_weight[:256, :]
										__magic_name__ :							int		       =							in_proj_bias[:256]
										__magic_name__ :							str		       =							in_proj_weight[256:512, :]
										__magic_name__ :							Optional[Any]		       =							in_proj_bias[256:512]
										__magic_name__ :							Tuple		       =							in_proj_weight[-256:, :]
										__magic_name__ :							Any		       =							in_proj_bias[-256:]







def 			lowerCAmelCase  (     )  ->Tuple:






					"""simple docstring"""






					__magic_name__ :							Optional[Any]		       =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					__magic_name__ :							Tuple		       =							Image.open(requests.get(UpperCAmelCase,     stream=UpperCAmelCase	).raw	)

					return im







@torch.no_grad()
def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     )  ->List[str]:






					"""simple docstring"""






					__magic_name__ :							Any		       =							DeformableDetrConfig()
					# set config attributes
					if single_scale:
										__magic_name__ :							Optional[Any]		       =							1
					__magic_name__ :							Union[str, Any]		       =							dilation
					__magic_name__ :							Dict		       =							with_box_refine
					__magic_name__ :							str		       =							two_stage
					# set labels
					__magic_name__ :							Union[str, Any]		       =							91
					__magic_name__ :							str		       =							'''huggingface/label-files'''
					__magic_name__ :							Optional[Any]		       =							'''coco-detection-id2label.json'''
					__magic_name__ :							Optional[int]		       =							json.load(open(cached_download(hf_hub_url(UpperCAmelCase,     UpperCAmelCase,     repo_type='''dataset'''	)	),     '''r'''	)	)
					__magic_name__ :							Dict		       =							{int(UpperCAmelCase	): v for k, v in idalabel.items()}
					__magic_name__ :							Tuple		       =							idalabel
					__magic_name__ :							Optional[Any]		       =							{v: k for k, v in idalabel.items()}

					# load image processor
					__magic_name__ :							Union[str, Any]		       =							DeformableDetrImageProcessor(format='''coco_detection'''	)

					# prepare image
					__magic_name__ :							Optional[int]		       =							prepare_img()
					__magic_name__ :							Dict		       =							image_processor(images=UpperCAmelCase,     return_tensors='''pt'''	)
					__magic_name__ :							str		       =							encoding['''pixel_values''']

					logger.info('''Converting model...'''	)

					# load original state dict
					__magic_name__ :							int		       =							torch.load(UpperCAmelCase,     map_location='''cpu'''	)['''model''']
					# rename keys
					for key in state_dict.copy().keys():
										__magic_name__ :							Tuple		       =							state_dict.pop(UpperCAmelCase	)
										__magic_name__ :							List[str]		       =							val
					# query, key and value matrices need special treatment
					read_in_q_k_v(UpperCAmelCase	)
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					__magic_name__ :							str		       =							'''model.'''
					for key in state_dict.copy().keys():
										if not key.startswith('''class_embed'''	) and not key.startswith('''bbox_embed'''	):
															__magic_name__ :							Union[str, Any]		       =							state_dict.pop(UpperCAmelCase	)
															__magic_name__ :							List[str]		       =							val
    # finally, create HuggingFace model and load state dict
					__magic_name__ :							List[Any]		       =							DeformableDetrForObjectDetection(UpperCAmelCase	)
					model.load_state_dict(UpperCAmelCase	)
					model.eval()

					__magic_name__ :							List[str]		       =							'''cuda''' if torch.cuda.is_available() else '''cpu'''
					model.to(UpperCAmelCase	)
					# verify our conversion
					__magic_name__ :							Tuple		       =							model(pixel_values.to(UpperCAmelCase	)	)

					__magic_name__ :							Dict		       =							torch.tensor(
					    [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]	)
					__magic_name__ :							List[str]		       =							torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]	)

					if single_scale:
										__magic_name__ :							Dict		       =							torch.tensor(
										    [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]	)
										__magic_name__ :							Dict		       =							torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]	)

					if single_scale and dilation:
										__magic_name__ :							Optional[int]		       =							torch.tensor(
										    [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]	)
										__magic_name__ :							Any		       =							torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]	)

					if with_box_refine:
										__magic_name__ :							int		       =							torch.tensor(
										    [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]	)
										__magic_name__ :							str		       =							torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]	)

					if with_box_refine and two_stage:
										__magic_name__ :							Optional[Any]		       =							torch.tensor(
										    [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]	)
										__magic_name__ :							int		       =							torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]	)

					print('''Logits:''',     outputs.logits[0, :3, :3]	)

					assert torch.allclose(outputs.logits[0, :3, :3],     expected_logits.to(UpperCAmelCase	),     atol=1E-4	)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3],     expected_boxes.to(UpperCAmelCase	),     atol=1E-4	)

					print('''Everything ok!'''	)

					# 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	)

					# Push to hub
					if push_to_hub:
										__magic_name__ :							Dict		       =							'''deformable-detr'''
										model_name += "-single-scale" if single_scale else ""
										model_name += "-dc5" if dilation else ""
										model_name += "-with-box-refine" if with_box_refine else ""
										model_name += "-two-stage" if two_stage else ""
										print('''Pushing model to hub...'''	)
										model.push_to_hub(repo_path_or_name=UpperCAmelCase,     organization='''nielsr''',     commit_message='''Add model'''	)


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

				parser.add_argument(
				    '''--checkpoint_path''',
				    type=str,
				    default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
				    help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
				)
				parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
				parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
				parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
				parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
				parser.add_argument(
				    '''--pytorch_dump_folder_path''',
				    default=None,
				    type=str,
				    required=True,
				    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.'''
				)
				lowercase_		=							parser.parse_args()
				convert_deformable_detr_checkpoint(
				    args.checkpoint_path,
				    args.single_scale,
				    args.dilation,
				    args.with_box_refine,
				    args.two_stage,
				    args.pytorch_dump_folder_path,
				    args.push_to_hub,
				)









import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


lowercase_		=							'''./tests/fixtures/elise_format0.mid'''





@unittest.skip("The note-seq package currently throws an error on import"			)
class 		A__    (       __SCREAMING_SNAKE_CASE		,					unittest.TestCase			):
	lowerCamelCase__		:			Optional[int]       =SpectrogramDiffusionPipeline
	lowerCamelCase__		:			List[str]       =PipelineTesterMixin.required_optional_params - {
	    "callback",
	    "latents",
	    "callback_steps",
	    "output_type",
	    "num_images_per_prompt",
	}
	lowerCamelCase__		:			Union[str, Any]       =False
	lowerCamelCase__		:			List[Any]       =False
	lowerCamelCase__		:			List[str]       =TOKENS_TO_AUDIO_GENERATION_PARAMS
	lowerCamelCase__		:			str       =TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						torch.manual_seed(0						)
						__magic_name__ :							Optional[Any]		       =							SpectrogramNotesEncoder(
						    max_length=2048    ,					vocab_size=1536    ,					d_model=768    ,					dropout_rate=0.1    ,					num_layers=1    ,					num_heads=1    ,					d_kv=4    ,					d_ff=2048    ,					feed_forward_proj='''gated-gelu'''    ,					)

						__magic_name__ :							Any		       =							SpectrogramContEncoder(
						    input_dims=128    ,					targets_context_length=256    ,					d_model=768    ,					dropout_rate=0.1    ,					num_layers=1    ,					num_heads=1    ,					d_kv=4    ,					d_ff=2048    ,					feed_forward_proj='''gated-gelu'''    ,					)

						__magic_name__ :							List[Any]		       =							TaFilmDecoder(
						    input_dims=128    ,					targets_length=256    ,					max_decoder_noise_time=2_0_0_0_0.0    ,					d_model=768    ,					num_layers=1    ,					num_heads=1    ,					d_kv=4    ,					d_ff=2048    ,					dropout_rate=0.1    ,					)

						__magic_name__ :							Any		       =							DDPMScheduler()

						__magic_name__ :							Dict		       =							{
						    '''notes_encoder''': notes_encoder.eval(),
						    '''continuous_encoder''': continuous_encoder.eval(),
						    '''decoder''': decoder.eval(),
						    '''scheduler''': scheduler,
						    '''melgan''': None,
						}
						return components






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=0						)				->       Dict:




						"""simple docstring"""






						if str(lowerCamelCase						).startswith('''mps'''						):
											__magic_name__ :							List[str]		       =							torch.manual_seed(lowerCamelCase						)
						else:
											__magic_name__ :							Any		       =							torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
						__magic_name__ :							Any		       =							{
						    '''input_tokens''': [
						        [1134, 90, 1135, 1133, 1080, 112, 1132, 1080, 1133, 1079, 133, 1132, 1079, 1133, 1] + [0] * 2033
						    ],
						    '''generator''': generator,
						    '''num_inference_steps''': 4,
						    '''output_type''': '''mel''',
						}
						return inputs






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




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						__magic_name__ :							Union[str, Any]		       =							self.get_dummy_components()
						__magic_name__ :							str		       =							SpectrogramDiffusionPipeline(**lowerCamelCase						)
						__magic_name__ :							List[Any]		       =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)

						__magic_name__ :							Tuple		       =							self.get_dummy_inputs(lowerCamelCase						)
						__magic_name__ :							List[str]		       =							pipe(**lowerCamelCase						)
						__magic_name__ :							Any		       =							output.audios

						__magic_name__ :							Union[str, Any]		       =							mel[0, -3:, -3:]

						assert mel_slice.shape == (3, 3)
						__magic_name__ :							Dict		       =							np.array(
						    [-1_1.5_1_2_9_2_5, -4.7_8_8_2_1_5, -0.4_6_1_7_2_9_0_5, -2.0_5_1_7_1_5, -1_0.5_3_9_1_4_7, -1_0.9_7_0_9_6_3, -9.0_9_1_6_3_4, 4.0, 4.0]						)
						assert np.abs(mel_slice.flatten() - expected_slice						).max() < 1e-2






	@skip_mps
	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						return super().test_save_load_local()






	@skip_mps
	def 			lowercase				(     self						)				->       str:




						"""simple docstring"""






						return super().test_dict_tuple_outputs_equivalent()






	@skip_mps
	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						return super().test_save_load_optional_components()






	@skip_mps
	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						return super().test_attention_slicing_forward_pass()






	def 			lowercase				(     self						)				->       Tuple:




						"""simple docstring"""






						pass






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						pass






	@skip_mps
	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						return super().test_progress_bar()





@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class 		A__    (       unittest.TestCase			):






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




						"""simple docstring"""






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






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




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							torch_device

						__magic_name__ :							Any		       =							SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''						)
						__magic_name__ :							Optional[int]		       =							pipe.melgan
						__magic_name__ :							List[str]		       =							None

						__magic_name__ :							Tuple		       =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)

						def callback(lowerCamelCase    ,					lowerCamelCase						):
											# decode mel to audio
											__magic_name__ :							Tuple		       =							melgan(input_features=mel_output.astype(np.floataa						)						)[0]
											assert len(audio[0]						) == 81920 * (step + 1)
											# simulate that audio is played
											return audio

						__magic_name__ :							int		       =							MidiProcessor()
						__magic_name__ :							List[str]		       =							processor(lowerCamelCase						)

						__magic_name__ :							Dict		       =							input_tokens[:3]
						__magic_name__ :							Any		       =							torch.manual_seed(0						)
						pipe(lowerCamelCase    ,					num_inference_steps=5    ,					generator=lowerCamelCase    ,					callback=lowerCamelCase    ,					output_type='''mel'''						)






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							torch_device

						__magic_name__ :							int		       =							SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''						)
						__magic_name__ :							str		       =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)
						__magic_name__ :							Any		       =							MidiProcessor()

						__magic_name__ :							Optional[Any]		       =							processor(lowerCamelCase						)
						# just run two denoising loops
						__magic_name__ :							Any		       =							input_tokens[:2]

						__magic_name__ :							int		       =							torch.manual_seed(0						)
						__magic_name__ :							Any		       =							pipe(lowerCamelCase    ,					num_inference_steps=2    ,					generator=lowerCamelCase						)

						__magic_name__ :							Union[str, Any]		       =							output.audios[0]

						assert abs(np.abs(lowerCamelCase						).sum() - 3_6_1_2.8_4_1						) < 1e-1






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




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							torch_device

						__magic_name__ :							str		       =							SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''						)
						__magic_name__ :							List[Any]		       =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							MidiProcessor()

						__magic_name__ :							str		       =							processor(lowerCamelCase						)

						# just run 4 denoising loops
						__magic_name__ :							List[str]		       =							input_tokens[:4]

						__magic_name__ :							Union[str, Any]		       =							torch.manual_seed(0						)
						__magic_name__ :							Dict		       =							pipe(lowerCamelCase    ,					num_inference_steps=100    ,					generator=lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							output.audios[0]
						assert abs(np.abs(lowerCamelCase						).sum() - 9_3_8_9.1_1_1_1						) < 5e-2



