




from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Tuple      	=		['vqvae']






	def __init__(					self		:						Union[str, Any]					,     lowerCAmelCase		:						AutoencoderKL					,     lowerCAmelCase		:						UNetaDConditionModel					,     lowerCAmelCase		:						Mel					,     lowerCAmelCase		:						Union[DDIMScheduler, DDPMScheduler]					,     )	->       Dict:







						"""simple docstring"""







						super().__init__()
						self.register_modules(unet=lowerCAmelCase					,     scheduler=lowerCAmelCase					,     mel=lowerCAmelCase					,     vqvae=lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						List[str]	)	->       int:







						"""simple docstring"""







						return 50 if isinstance(self.scheduler					,     lowerCAmelCase	) else 1000






	@torch.no_grad()
	def __call__(					self		:						str					,     lowerCAmelCase		:						int = 1					,     lowerCAmelCase		:						str = None					,     lowerCAmelCase		:						np.ndarray = None					,     lowerCAmelCase		:						int = 0					,     lowerCAmelCase		:						int = 0					,     lowerCAmelCase		:						int = None					,     lowerCAmelCase		:						torch.Generator = None					,     lowerCAmelCase		:						float = 0					,     lowerCAmelCase		:						float = 0					,     lowerCAmelCase		:						torch.Generator = None					,     lowerCAmelCase		:						float = 0					,     lowerCAmelCase		:						torch.Tensor = None					,     lowerCAmelCase		:						torch.Tensor = None					,     lowerCAmelCase		:						Optional[int]=True					,     )	->       Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      steps or self.get_default_steps()
						self.scheduler.set_timesteps(lowerCAmelCase	)
						__UpperCamelCase					:	Tuple             =      step_generator or generator
						# For backwards compatibility
						if type(self.unet.config.sample_size	) == int:
											__UpperCamelCase					:	Dict             =      (self.unet.config.sample_size, self.unet.config.sample_size)
						if noise is None:
											__UpperCamelCase					:	List[str]             =      randn_tensor(
											    (
											        batch_size,
											        self.unet.config.in_channels,
											        self.unet.config.sample_size[0],
											        self.unet.config.sample_size[1],
											    )					,     generator=lowerCAmelCase					,     device=self.device					,     )
						__UpperCamelCase					:	int             =      noise
						__UpperCamelCase					:	List[str]             =      None

						if audio_file is not None or raw_audio is not None:
											self.mel.load_audio(lowerCAmelCase					,     lowerCAmelCase	)
											__UpperCamelCase					:	int             =      self.mel.audio_slice_to_image(lowerCAmelCase	)
											__UpperCamelCase					:	List[Any]             =      np.frombuffer(input_image.tobytes()					,     dtype="""uint8"""	).reshape(
											    (input_image.height, input_image.width)	)
											__UpperCamelCase					:	Optional[int]             =      (input_image / 255) * 2 - 1
											__UpperCamelCase					:	List[Any]             =      torch.tensor(input_image[np.newaxis, :, :]					,     dtype=torch.float	).to(self.device	)

											if self.vqvae is not None:
																__UpperCamelCase					:	Tuple             =      self.vqvae.encode(torch.unsqueeze(lowerCAmelCase					,     0	)	).latent_dist.sample(
																    generator=lowerCAmelCase	)[0]
																__UpperCamelCase					:	Any             =      self.vqvae.config.scaling_factor * input_images

											if start_step > 0:
																__UpperCamelCase					:	Union[str, Any]             =      self.scheduler.add_noise(lowerCAmelCase					,     lowerCAmelCase					,     self.scheduler.timesteps[start_step - 1]	)

											__UpperCamelCase					:	str             =      (
											    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
											)
											__UpperCamelCase					:	int             =      int(mask_start_secs * pixels_per_second	)
											__UpperCamelCase					:	Optional[int]             =      int(mask_end_secs * pixels_per_second	)
											__UpperCamelCase					:	Any             =      self.scheduler.add_noise(lowerCAmelCase					,     lowerCAmelCase					,     torch.tensor(self.scheduler.timesteps[start_step:]	)	)

						for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]	)	):
											if isinstance(self.unet					,     lowerCAmelCase	):
																__UpperCamelCase					:	Dict             =      self.unet(lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase	)["""sample"""]
											else:
																__UpperCamelCase					:	int             =      self.unet(lowerCAmelCase					,     lowerCAmelCase	)["""sample"""]

											if isinstance(self.scheduler					,     lowerCAmelCase	):
																__UpperCamelCase					:	List[Any]             =      self.scheduler.step(
																    model_output=lowerCAmelCase					,     timestep=lowerCAmelCase					,     sample=lowerCAmelCase					,     eta=lowerCAmelCase					,     generator=lowerCAmelCase					,     )["""prev_sample"""]
											else:
																__UpperCamelCase					:	Optional[Any]             =      self.scheduler.step(
																    model_output=lowerCAmelCase					,     timestep=lowerCAmelCase					,     sample=lowerCAmelCase					,     generator=lowerCAmelCase					,     )["""prev_sample"""]

											if mask is not None:
																if mask_start > 0:
																					__UpperCamelCase					:	List[Any]             =      mask[:, step, :, :mask_start]
																if mask_end > 0:
																					__UpperCamelCase					:	Tuple             =      mask[:, step, :, -mask_end:]

						if self.vqvae is not None:
											# 0.18215 was scaling factor used in training to ensure unit variance
											__UpperCamelCase					:	Union[str, Any]             =      1 / self.vqvae.config.scaling_factor * images
											__UpperCamelCase					:	Optional[Any]             =      self.vqvae.decode(lowerCAmelCase	)["""sample"""]

						__UpperCamelCase					:	str             =      (images / 2 + 0.5).clamp(0					,     1	)
						__UpperCamelCase					:	Optional[int]             =      images.cpu().permute(0					,     2					,     3					,     1	).numpy()
						__UpperCamelCase					:	Dict             =      (images * 255).round().astype("""uint8"""	)
						__UpperCamelCase					:	Tuple             =      list(
						    (Image.fromarray(_[:, :, 0]	) for _ in images)
						    if images.shape[3] == 1
						    else (Image.fromarray(lowerCAmelCase					,     mode="""RGB"""	).convert("""L"""	) for _ in images)	)

						__UpperCamelCase					:	Optional[Any]             =      [self.mel.image_to_audio(lowerCAmelCase	) for _ in images]
						if not return_dict:
											return images, (self.mel.get_sample_rate(), audios)

						return BaseOutput(**AudioPipelineOutput(np.array(lowerCAmelCase	)[:, np.newaxis, :]	)					,     **ImagePipelineOutput(lowerCAmelCase	)	)






	@torch.no_grad()
	def 					lowerCamelCase__     (					self		:						Tuple					,     lowerCAmelCase		:						List[Image.Image]					,     lowerCAmelCase		:						int = 50	)	->       np.ndarray:







						"""simple docstring"""







						assert isinstance(self.scheduler					,     lowerCAmelCase	)
						self.scheduler.set_timesteps(lowerCAmelCase	)
						__UpperCamelCase					:	List[Any]             =      np.array(
						    [np.frombuffer(image.tobytes()					,     dtype="""uint8"""	).reshape((1, image.height, image.width)	) for image in images]	)
						__UpperCamelCase					:	Dict             =      (sample / 255) * 2 - 1
						__UpperCamelCase					:	Dict             =      torch.Tensor(lowerCAmelCase	).to(self.device	)

						for t in self.progress_bar(torch.flip(self.scheduler.timesteps					,     (0,)	)	):
											__UpperCamelCase					:	Optional[Any]             =      t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
											__UpperCamelCase					:	Any             =      self.scheduler.alphas_cumprod[t]
											__UpperCamelCase					:	Tuple             =      (
											    self.scheduler.alphas_cumprod[prev_timestep]
											    if prev_timestep >= 0
											    else self.scheduler.final_alpha_cumprod
											)
											__UpperCamelCase					:	Optional[int]             =      1 - alpha_prod_t
											__UpperCamelCase					:	Tuple             =      self.unet(lowerCAmelCase					,     lowerCAmelCase	)["""sample"""]
											__UpperCamelCase					:	Union[str, Any]             =      (1 - alpha_prod_t_prev) ** 0.5 * model_output
											__UpperCamelCase					:	str             =      (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
											__UpperCamelCase					:	List[str]             =      sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

						return sample






	@staticmethod
	def 					lowerCamelCase__     (					lowerCAmelCase		:						torch.Tensor					,     lowerCAmelCase		:						torch.Tensor					,     lowerCAmelCase		:						float	)	->       torch.Tensor:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      acos(torch.dot(torch.flatten(lowerCAmelCase	)					,     torch.flatten(lowerCAmelCase	)	) / torch.norm(lowerCAmelCase	) / torch.norm(lowerCAmelCase	)	)
						return sin((1 - alpha) * theta	) * xa / sin(lowerCAmelCase	) + sin(alpha * theta	) * xa / sin(lowerCAmelCase	)












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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


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





def 				A__ (snake_case   :		str				)     ->						YolosConfig:
					__UpperCamelCase					:	Dict             =      YolosConfig()

					# size of the architecture
					if "yolos_ti" in yolos_name:
										__UpperCamelCase					:	Union[str, Any]             =      1_92
										__UpperCamelCase					:	Optional[int]             =      7_68
										__UpperCamelCase					:	List[Any]             =      12
										__UpperCamelCase					:	Optional[int]             =      3
										__UpperCamelCase					:	Optional[Any]             =      [8_00, 13_33]
										__UpperCamelCase					:	Optional[Any]             =      False
					elif yolos_name == "yolos_s_dWr":
										__UpperCamelCase					:	Union[str, Any]             =      3_30
										__UpperCamelCase					:	List[Any]             =      14
										__UpperCamelCase					:	Dict             =      6
										__UpperCamelCase					:	Optional[int]             =      13_20
					elif "yolos_s" in yolos_name:
										__UpperCamelCase					:	Dict             =      3_84
										__UpperCamelCase					:	Optional[int]             =      15_36
										__UpperCamelCase					:	Optional[int]             =      12
										__UpperCamelCase					:	List[str]             =      6
					elif "yolos_b" in yolos_name:
										__UpperCamelCase					:	List[Any]             =      [8_00, 13_44]

					__UpperCamelCase					:	Optional[Any]             =      91
					__UpperCamelCase					:	Union[str, Any]             =      """huggingface/label-files"""
					__UpperCamelCase					:	List[Any]             =      """coco-detection-id2label.json"""
					__UpperCamelCase					:	Any             =      json.load(open(hf_hub_download(snake_case		,	snake_case		,	repo_type="""dataset"""				)		,	"""r"""				)				)
					__UpperCamelCase					:	Tuple             =      {int(snake_case				): v for k, v in idalabel.items()}
					__UpperCamelCase					:	str             =      idalabel
					__UpperCamelCase					:	List[Any]             =      {v: k for k, v in idalabel.items()}

					return config





def 				A__ (snake_case   :		dict		,	snake_case   :		YolosConfig		,	snake_case   :		bool = False				)     ->						List[Any]:
					for i in range(config.num_hidden_layers				):
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										__UpperCamelCase					:	Tuple             =      state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''				)
										__UpperCamelCase					:	str             =      state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''				)
										# next, add query, keys and values (in that order) to the state dict
										__UpperCamelCase					:	List[Any]             =      in_proj_weight[: config.hidden_size, :]
										__UpperCamelCase					:	Union[str, Any]             =      in_proj_bias[: config.hidden_size]
										__UpperCamelCase					:	Union[str, Any]             =      in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__UpperCamelCase					:	Tuple             =      in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										__UpperCamelCase					:	Tuple             =      in_proj_weight[-config.hidden_size :, :]
										__UpperCamelCase					:	List[Any]             =      in_proj_bias[-config.hidden_size :]





def 				A__ (snake_case   :		str				)     ->						str:
					if "backbone" in name:
										__UpperCamelCase					:	str             =      name.replace("""backbone"""		,	"""vit"""				)
					if "cls_token" in name:
										__UpperCamelCase					:	Any             =      name.replace("""cls_token"""		,	"""embeddings.cls_token"""				)
					if "det_token" in name:
										__UpperCamelCase					:	Tuple             =      name.replace("""det_token"""		,	"""embeddings.detection_tokens"""				)
					if "mid_pos_embed" in name:
										__UpperCamelCase					:	int             =      name.replace("""mid_pos_embed"""		,	"""encoder.mid_position_embeddings"""				)
					if "pos_embed" in name:
										__UpperCamelCase					:	int             =      name.replace("""pos_embed"""		,	"""embeddings.position_embeddings"""				)
					if "patch_embed.proj" in name:
										__UpperCamelCase					:	Tuple             =      name.replace("""patch_embed.proj"""		,	"""embeddings.patch_embeddings.projection"""				)
					if "blocks" in name:
										__UpperCamelCase					:	Any             =      name.replace("""blocks"""		,	"""encoder.layer"""				)
					if "attn.proj" in name:
										__UpperCamelCase					:	Dict             =      name.replace("""attn.proj"""		,	"""attention.output.dense"""				)
					if "attn" in name:
										__UpperCamelCase					:	str             =      name.replace("""attn"""		,	"""attention.self"""				)
					if "norm1" in name:
										__UpperCamelCase					:	Union[str, Any]             =      name.replace("""norm1"""		,	"""layernorm_before"""				)
					if "norm2" in name:
										__UpperCamelCase					:	Optional[int]             =      name.replace("""norm2"""		,	"""layernorm_after"""				)
					if "mlp.fc1" in name:
										__UpperCamelCase					:	Tuple             =      name.replace("""mlp.fc1"""		,	"""intermediate.dense"""				)
					if "mlp.fc2" in name:
										__UpperCamelCase					:	Union[str, Any]             =      name.replace("""mlp.fc2"""		,	"""output.dense"""				)
					if "class_embed" in name:
										__UpperCamelCase					:	Tuple             =      name.replace("""class_embed"""		,	"""class_labels_classifier"""				)
					if "bbox_embed" in name:
										__UpperCamelCase					:	Dict             =      name.replace("""bbox_embed"""		,	"""bbox_predictor"""				)
					if "vit.norm" in name:
										__UpperCamelCase					:	Dict             =      name.replace("""vit.norm"""		,	"""vit.layernorm"""				)

					return name





def 				A__ (snake_case   :		dict		,	snake_case   :		YolosForObjectDetection				)     ->						dict:
					for key in orig_state_dict.copy().keys():
										__UpperCamelCase					:	Dict             =      orig_state_dict.pop(snake_case				)

										if "qkv" in key:
															__UpperCamelCase					:	Optional[int]             =      key.split("""."""				)
															__UpperCamelCase					:	Union[str, Any]             =      int(key_split[2]				)
															__UpperCamelCase					:	Any             =      model.vit.encoder.layer[layer_num].attention.attention.all_head_size
															if "weight" in key:
																				__UpperCamelCase					:	Tuple             =      val[:dim, :]
																				__UpperCamelCase					:	str             =      val[
																				    dim : dim * 2, :
																				]
																				__UpperCamelCase					:	int             =      val[-dim:, :]
															else:
																				__UpperCamelCase					:	Tuple             =      val[:dim]
																				__UpperCamelCase					:	Any             =      val[dim : dim * 2]
																				__UpperCamelCase					:	str             =      val[-dim:]
										else:
															__UpperCamelCase					:	Optional[int]             =      val

					return orig_state_dict





def 				A__ ()     ->						torch.Tensor:
					__UpperCamelCase					:	Any             =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
					__UpperCamelCase					:	Any             =      Image.open(requests.get(snake_case		,	stream=snake_case				).raw				)
					return im





@torch.no_grad()
def 				A__ (snake_case   :		str		,	snake_case   :		str		,	snake_case   :		str		,	snake_case   :		bool = False				)     ->						Optional[Any]:
					__UpperCamelCase					:	List[str]             =      get_yolos_config(snake_case				)

					# load original state_dict
					__UpperCamelCase					:	Tuple             =      torch.load(snake_case		,	map_location="""cpu"""				)["""model"""]

					# load 🤗 model
					__UpperCamelCase					:	Dict             =      YolosForObjectDetection(snake_case				)
					model.eval()
					__UpperCamelCase					:	int             =      convert_state_dict(snake_case		,	snake_case				)
					model.load_state_dict(snake_case				)

					# Check outputs on an image, prepared by YolosImageProcessor
					__UpperCamelCase					:	Any             =      8_00 if yolos_name != """yolos_ti""" else 5_12
					__UpperCamelCase					:	int             =      YolosImageProcessor(format="""coco_detection"""		,	size=snake_case				)
					__UpperCamelCase					:	Any             =      image_processor(images=prepare_img()		,	return_tensors="""pt"""				)
					__UpperCamelCase					:	List[Any]             =      model(**snake_case				)
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      outputs.logits, outputs.pred_boxes

					__UpperCamelCase      ,			__UpperCamelCase					:	List[Any]             =      None, None
					if yolos_name == "yolos_ti":
										__UpperCamelCase					:	Dict             =      torch.tensor(
										    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]				)
										__UpperCamelCase					:	Union[str, Any]             =      torch.tensor(
										    [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]				)
					elif yolos_name == "yolos_s_200_pre":
										__UpperCamelCase					:	Optional[Any]             =      torch.tensor(
										    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]				)
										__UpperCamelCase					:	str             =      torch.tensor(
										    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]				)
					elif yolos_name == "yolos_s_300_pre":
										__UpperCamelCase					:	Union[str, Any]             =      torch.tensor(
										    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]				)
										__UpperCamelCase					:	Tuple             =      torch.tensor(
										    [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]				)
					elif yolos_name == "yolos_s_dWr":
										__UpperCamelCase					:	str             =      torch.tensor(
										    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]				)
										__UpperCamelCase					:	Optional[Any]             =      torch.tensor(
										    [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]				)
					elif yolos_name == "yolos_base":
										__UpperCamelCase					:	Optional[int]             =      torch.tensor(
										    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]				)
										__UpperCamelCase					:	List[str]             =      torch.tensor(
										    [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]				)
					else:
										raise ValueError(F'''Unknown yolos_name: {yolos_name}'''				)

					assert torch.allclose(logits[0, :3, :3]		,	snake_case		,	atol=1e-4				)
					assert torch.allclose(pred_boxes[0, :3, :3]		,	snake_case		,	atol=1e-4				)

					Path(snake_case				).mkdir(exist_ok=snake_case				)
					print(F'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''				)
					model.save_pretrained(snake_case				)
					print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
					image_processor.save_pretrained(snake_case				)

					if push_to_hub:
										__UpperCamelCase					:	Tuple             =      {
										    """yolos_ti""": """yolos-tiny""",
										    """yolos_s_200_pre""": """yolos-small""",
										    """yolos_s_300_pre""": """yolos-small-300""",
										    """yolos_s_dWr""": """yolos-small-dwr""",
										    """yolos_base""": """yolos-base""",
										}

										print("""Pushing to the hub..."""				)
										__UpperCamelCase					:	int             =      model_mapping[yolos_name]
										image_processor.push_to_hub(snake_case		,	organization="""hustvl"""				)
										model.push_to_hub(snake_case		,	organization="""hustvl"""				)


if __name__ == "__main__":
	a__		     =     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--yolos_name''',
	    default='''yolos_s_200_pre''',
	    type=str,
	    help=(
	        '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
	        ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
	    ),
	)
	parser.add_argument(
	    '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
	)
	parser.add_argument(
	    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
	)

	a__		     =     parser.parse_args()
	convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






