



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   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		str      							=     ["vqvae"]



					def __init__(  self       :							int					,       _snake_case       :							AutoencoderKL					,       _snake_case       :							UNetaDConditionModel					,       _snake_case       :							Mel					,       _snake_case       :							Union[DDIMScheduler, DDPMScheduler]					,       ):






								"""simple docstring"""


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



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								return 50 if isinstance(self.scheduler					,       _snake_case       ) else 10_00



					@torch.no_grad()
					def __call__(  self       :							List[str]					,       _snake_case       :							int = 1					,       _snake_case       :							str = None					,       _snake_case       :							np.ndarray = None					,       _snake_case       :							int = 0					,       _snake_case       :							int = 0					,       _snake_case       :							int = None					,       _snake_case       :							torch.Generator = None					,       _snake_case       :							float = 0					,       _snake_case       :							float = 0					,       _snake_case       :							torch.Generator = None					,       _snake_case       :							float = 0					,       _snake_case       :							torch.Tensor = None					,       _snake_case       :							torch.Tensor = None					,       _snake_case       :							int=True					,       ):






								"""simple docstring"""


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

								if audio_file is not None or raw_audio is not None:
											self.mel.load_audio(_snake_case					,       _snake_case       )
											A__	  =     self.mel.audio_slice_to_image(_snake_case       )
											A__	  =     np.frombuffer(input_image.tobytes()					,       dtype='uint8'       ).reshape(
											    (input_image.height, input_image.width)       )
											A__	  =     (input_image / 2_55) * 2 - 1
											A__	  =     torch.tensor(input_image[np.newaxis, :, :]					,       dtype=torch.float       ).to(self.device       )

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

											if start_step > 0:
														A__	  =     self.scheduler.add_noise(_snake_case					,       _snake_case					,       self.scheduler.timesteps[start_step - 1]       )

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

								for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]       )       ):
											if isinstance(self.unet					,       _snake_case       ):
														A__	  =     self.unet(_snake_case					,       _snake_case					,       _snake_case       )['sample']
											else:
														A__	  =     self.unet(_snake_case					,       _snake_case       )['sample']

											if isinstance(self.scheduler					,       _snake_case       ):
														A__	  =     self.scheduler.step(
														    model_output=_snake_case					,       timestep=_snake_case					,       sample=_snake_case					,       eta=_snake_case					,       generator=_snake_case					,       )['prev_sample']
											else:
														A__	  =     self.scheduler.step(
														    model_output=_snake_case					,       timestep=_snake_case					,       sample=_snake_case					,       generator=_snake_case					,       )['prev_sample']

											if mask is not None:
														if mask_start > 0:
																	A__	  =     mask[:, step, :, :mask_start]
														if mask_end > 0:
																	A__	  =     mask[:, step, :, -mask_end:]

								if self.vqvae is not None:
											# 0.18215 was scaling factor used in training to ensure unit variance
											A__	  =     1 / self.vqvae.config.scaling_factor * images
											A__	  =     self.vqvae.decode(_snake_case       )['sample']

								A__	  =     (images / 2 + 0.5).clamp(0					,       1       )
								A__	  =     images.cpu().permute(0					,       2					,       3					,       1       ).numpy()
								A__	  =     (images * 2_55).round().astype('uint8'       )
								A__	  =     list(
								    (Image.fromarray(_[:, :, 0]       ) for _ in images)
								    if images.shape[3] == 1
								    else (Image.fromarray(_snake_case					,       mode='RGB'       ).convert('L'       ) for _ in images)       )

								A__	  =     [self.mel.image_to_audio(_snake_case       ) for _ in images]
								if not return_dict:
											return images, (self.mel.get_sample_rate(), audios)

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



					@torch.no_grad()
					def      _a				(  self       :							Optional[Any]					,       _snake_case       :							List[Image.Image]					,       _snake_case       :							int = 50       ):






								"""simple docstring"""


								assert isinstance(self.scheduler					,       _snake_case       )
								self.scheduler.set_timesteps(_snake_case       )
								A__	  =     np.array(
								    [np.frombuffer(image.tobytes()					,       dtype='uint8'       ).reshape((1, image.height, image.width)       ) for image in images]       )
								A__	  =     (sample / 2_55) * 2 - 1
								A__	  =     torch.Tensor(_snake_case       ).to(self.device       )

								for t in self.progress_bar(torch.flip(self.scheduler.timesteps					,       (0,)       )       ):
											A__	  =     t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
											A__	  =     self.scheduler.alphas_cumprod[t]
											A__	  =     (
											    self.scheduler.alphas_cumprod[prev_timestep]
											    if prev_timestep >= 0
											    else self.scheduler.final_alpha_cumprod
											)
											A__	  =     1 - alpha_prod_t
											A__	  =     self.unet(_snake_case					,       _snake_case       )['sample']
											A__	  =     (1 - alpha_prod_t_prev) ** 0.5 * model_output
											A__	  =     (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
											A__	  =     sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

								return sample



					@staticmethod
					def      _a				(  _snake_case       :							torch.Tensor					,       _snake_case       :							torch.Tensor					,       _snake_case       :							float       ):






								"""simple docstring"""


								A__	  =     acos(torch.dot(torch.flatten(_snake_case       )					,       torch.flatten(_snake_case       )       ) / torch.norm(_snake_case       ) / torch.norm(_snake_case       )       )
								return sin((1 - alpha) * theta       ) * xa / sin(_snake_case       ) + sin(alpha * theta       ) * xa / sin(_snake_case       )









import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


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


def A    (							__UpperCamelCase       )    ->   List[Any]:
			print('Loading config file...'       )

			def flatten_yaml_as_dict(__UpperCamelCase			,						__UpperCamelCase=""			,						__UpperCamelCase="."       ):
						A__	  =     []
						for k, v in d.items():
									A__	  =     parent_key + sep + k if parent_key else k
									if isinstance(__UpperCamelCase			,						collections.abc.MutableMapping       ):
												items.extend(flatten_yaml_as_dict(__UpperCamelCase			,						__UpperCamelCase			,						sep=__UpperCamelCase       ).items()       )
									else:
												items.append((new_key, v)       )
						return dict(__UpperCamelCase       )

			A__	  =     argparse.Namespace()
			with open(__UpperCamelCase			,						'r'       ) as yaml_file:
						try:
									A__	  =     yaml.load(__UpperCamelCase			,						Loader=yaml.FullLoader       )

									A__	  =     flatten_yaml_as_dict(__UpperCamelCase       )
									for k, v in flat_cfg.items():
												setattr(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
						except yaml.YAMLError as exc:
									logger.error('Error while loading config file: {}. Error message: {}'.format(__UpperCamelCase			,						str(__UpperCamelCase       )       )       )
			return config


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     MobileViTVaConfig()

			A__	  =     False

			# dataset
			if task_name.startswith('imagenet1k_'       ):
						A__	  =     1_000
						if int(task_name.strip().split('_'       )[-1]       ) == 384:
									A__	  =     384
						else:
									A__	  =     256
						A__	  =     'imagenet-1k-id2label.json'
			elif task_name.startswith('imagenet21k_to_1k_'       ):
						A__	  =     21_000
						if int(task_name.strip().split('_'       )[-1]       ) == 384:
									A__	  =     384
						else:
									A__	  =     256
						A__	  =     'imagenet-22k-id2label.json'
			elif task_name.startswith('ade20k_'       ):
						A__	  =     151
						A__	  =     512
						A__	  =     'ade20k-id2label.json'
						A__	  =     True
			elif task_name.startswith('voc_'       ):
						A__	  =     21
						A__	  =     512
						A__	  =     'pascal-voc-id2label.json'
						A__	  =     True

			# orig_config
			A__	  =     load_orig_config_file(__UpperCamelCase       )
			assert getattr(__UpperCamelCase			,						'model.classification.name'			,						-1       ) == "mobilevit_v2", "Invalid model"
			A__	  =     getattr(__UpperCamelCase			,						'model.classification.mitv2.width_multiplier'			,						1.0       )
			assert (
			    getattr(__UpperCamelCase			,						'model.classification.mitv2.attn_norm_layer'			,						-1       ) == "layer_norm_2d"
			), "Norm layers other than layer_norm_2d is not supported"
			A__	  =     getattr(__UpperCamelCase			,						'model.classification.activation.name'			,						'swish'       )
			# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

			if is_segmentation_model:
						A__	  =     getattr(__UpperCamelCase			,						'model.segmentation.output_stride'			,						16       )
						if "_deeplabv3" in task_name:
									A__	  =     getattr(__UpperCamelCase			,						'model.segmentation.deeplabv3.aspp_rates'			,						[12, 24, 36]       )
									A__	  =     getattr(__UpperCamelCase			,						'model.segmentation.deeplabv3.aspp_out_channels'			,						512       )
									A__	  =     getattr(__UpperCamelCase			,						'model.segmentation.deeplabv3.aspp_dropout'			,						0.1       )

    # id2label
			A__	  =     'huggingface/label-files'
			A__	  =     json.load(open(hf_hub_download(__UpperCamelCase			,						__UpperCamelCase			,						repo_type='dataset'       )			,						'r'       )       )
			A__	  =     {int(__UpperCamelCase       ): v for k, v in idalabel.items()}
			A__	  =     idalabel
			A__	  =     {v: k for k, v in idalabel.items()}

			return config


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   List[str]:
			A__	  =     dct.pop(__UpperCamelCase       )
			A__	  =     val


def A    (							__UpperCamelCase			,						__UpperCamelCase=False       )    ->   Dict:
			if base_model:
						A__	  =     ''
			else:
						A__	  =     'mobilevitv2.'

			A__	  =     []
			for k in state_dict.keys():
						if k[:8] == "encoder.":
									A__	  =     k[8:]
						else:
									A__	  =     k

						if ".block." in k:
									A__	  =     k_new.replace('.block.'			,						'.'       )
						if ".conv." in k:
									A__	  =     k_new.replace('.conv.'			,						'.convolution.'       )
						if ".norm." in k:
									A__	  =     k_new.replace('.norm.'			,						'.normalization.'       )

						if "conv_1." in k:
									A__	  =     k_new.replace('conv_1.'			,						f'''{model_prefix}conv_stem.'''       )
						for i in [1, 2]:
									if f'''layer_{i}.''' in k:
												A__	  =     k_new.replace(f'''layer_{i}.'''			,						f'''{model_prefix}encoder.layer.{i-1}.layer.'''       )
						if ".exp_1x1." in k:
									A__	  =     k_new.replace('.exp_1x1.'			,						'.expand_1x1.'       )
						if ".red_1x1." in k:
									A__	  =     k_new.replace('.red_1x1.'			,						'.reduce_1x1.'       )

						for i in [3, 4, 5]:
									if f'''layer_{i}.0.''' in k:
												A__	  =     k_new.replace(f'''layer_{i}.0.'''			,						f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''       )
									if f'''layer_{i}.1.local_rep.0.''' in k:
												A__	  =     k_new.replace(f'''layer_{i}.1.local_rep.0.'''			,						f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''       )
									if f'''layer_{i}.1.local_rep.1.''' in k:
												A__	  =     k_new.replace(f'''layer_{i}.1.local_rep.1.'''			,						f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''       )

						for i in [3, 4, 5]:
									if i == 3:
												A__	  =     [0, 1]
									elif i == 4:
												A__	  =     [0, 1, 2, 3]
									elif i == 5:
												A__	  =     [0, 1, 2]

									for j in j_in:
												if f'''layer_{i}.1.global_rep.{j}.''' in k:
															A__	  =     k_new.replace(
															    f'''layer_{i}.1.global_rep.{j}.'''			,						f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''       )
									if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
												A__	  =     k_new.replace(
												    f'''layer_{i}.1.global_rep.{j+1}.'''			,						f'''{model_prefix}encoder.layer.{i-1}.layernorm.'''       )

									if f'''layer_{i}.1.conv_proj.''' in k:
												A__	  =     k_new.replace(f'''layer_{i}.1.conv_proj.'''			,						f'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''       )

						if "pre_norm_attn.0." in k:
									A__	  =     k_new.replace('pre_norm_attn.0.'			,						'layernorm_before.'       )
						if "pre_norm_attn.1." in k:
									A__	  =     k_new.replace('pre_norm_attn.1.'			,						'attention.'       )
						if "pre_norm_ffn.0." in k:
									A__	  =     k_new.replace('pre_norm_ffn.0.'			,						'layernorm_after.'       )
						if "pre_norm_ffn.1." in k:
									A__	  =     k_new.replace('pre_norm_ffn.1.'			,						'ffn.conv1.'       )
						if "pre_norm_ffn.3." in k:
									A__	  =     k_new.replace('pre_norm_ffn.3.'			,						'ffn.conv2.'       )

						if "classifier.1." in k:
									A__	  =     k_new.replace('classifier.1.'			,						'classifier.'       )

						if "seg_head." in k:
									A__	  =     k_new.replace('seg_head.'			,						'segmentation_head.'       )
						if ".aspp_layer." in k:
									A__	  =     k_new.replace('.aspp_layer.'			,						'.'       )
						if ".aspp_pool." in k:
									A__	  =     k_new.replace('.aspp_pool.'			,						'.'       )

						rename_keys.append((k, k_new)       )
			return rename_keys


def A    (							__UpperCamelCase       )    ->   Tuple:
			A__	  =     []
			for k in state_dict.keys():
						if k.startswith('seg_head.aux_head.'       ):
									keys_to_ignore.append(__UpperCamelCase       )
			for k in keys_to_ignore:
						state_dict.pop(__UpperCamelCase			,						__UpperCamelCase       )


def A    (							)    ->   str:
			A__	  =     'http://images.cocodataset.org/val2017/000000039769.jpg'
			# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
			A__	  =     Image.open(requests.get(__UpperCamelCase			,						stream=__UpperCamelCase       ).raw       )
			return im


@torch.no_grad()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     get_mobilevitva_config(__UpperCamelCase			,						__UpperCamelCase       )

			# load original state_dict
			A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )

			# load huggingface model
			if task_name.startswith('ade20k_'       ) or task_name.startswith('voc_'       ):
						A__	  =     MobileViTVaForSemanticSegmentation(__UpperCamelCase       ).eval()
						A__	  =     False
			else:
						A__	  =     MobileViTVaForImageClassification(__UpperCamelCase       ).eval()
						A__	  =     False

			# remove and rename some keys of load the original model
			A__	  =     checkpoint
			remove_unused_keys(__UpperCamelCase       )
			A__	  =     create_rename_keys(__UpperCamelCase			,						base_model=__UpperCamelCase       )
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )

			# load modified state_dict
			model.load_state_dict(__UpperCamelCase       )

			# Check outputs on an image, prepared by MobileViTImageProcessor
			A__	  =     MobileViTImageProcessor(crop_size=config.image_size			,						size=config.image_size + 32       )
			A__	  =     image_processor(images=prepare_img()			,						return_tensors='pt'       )
			A__	  =     model(**__UpperCamelCase       )

			# verify classification model
			if task_name.startswith('imagenet'       ):
						A__	  =     outputs.logits
						A__	  =     logits.argmax(-1       ).item()
						print('Predicted class:'			,						model.config.idalabel[predicted_class_idx]       )
						if task_name.startswith('imagenet1k_256'       ) and config.width_multiplier == 1.0:
									# expected_logits for base variant
									A__	  =     torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]       )
									assert torch.allclose(logits[0, :3]			,						__UpperCamelCase			,						atol=1E-4       )

			Path(__UpperCamelCase       ).mkdir(exist_ok=__UpperCamelCase       )
			print(f'''Saving model {task_name} to {pytorch_dump_folder_path}'''       )
			model.save_pretrained(__UpperCamelCase       )
			print(f'''Saving image processor to {pytorch_dump_folder_path}'''       )
			image_processor.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--task''',
							    default='''imagenet1k_256''',
							    type=str,
							    help=(
							        '''Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '''
							        '''
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            '''
							    ),
							    choices=[
							        '''imagenet1k_256''',
							        '''imagenet1k_384''',
							        '''imagenet21k_to_1k_256''',
							        '''imagenet21k_to_1k_384''',
							        '''ade20k_deeplabv3''',
							        '''voc_deeplabv3''',
							    ],
							)

							parser.add_argument(
							    '''--orig_checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
							)
							parser.add_argument('''--orig_config_path''', required=True, type=str, help='''Path to the original config file.''')
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
							)

							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()
							convert_mobilevitva_checkpoint(
							    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
							)




