



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


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






					A__					:		int      							=     ["input_features"]



					def __init__(  self       :							Dict					,       _snake_case       :							str=80					,       _snake_case       :							List[Any]=1_60_00					,       _snake_case       :							str=1_60					,       _snake_case       :							Any=30					,       _snake_case       :							Dict=4_00					,       _snake_case       :							int=0.0					,       _snake_case       :							Tuple=False					,       **_snake_case       :							Optional[Any]					,       ):






								"""simple docstring"""


								super().__init__(
								    feature_size=_snake_case					,       sampling_rate=_snake_case					,       padding_value=_snake_case					,       return_attention_mask=_snake_case					,       **_snake_case					,       )
								A__	  =     n_fft
								A__	  =     hop_length
								A__	  =     chunk_length
								A__	  =     chunk_length * sampling_rate
								A__	  =     self.n_samples // hop_length
								A__	  =     sampling_rate
								A__	  =     mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2					,       num_mel_filters=_snake_case					,       min_frequency=0.0					,       max_frequency=8000.0					,       sampling_rate=_snake_case					,       norm='slaney'					,       mel_scale='slaney'					,       )



					def      _a				(  self       :							List[str]					,       _snake_case       :							np.array       ):






								"""simple docstring"""


								A__	  =     spectrogram(
								    _snake_case					,       window_function(self.n_fft					,       'hann'       )					,       frame_length=self.n_fft					,       hop_length=self.hop_length					,       power=2.0					,       mel_filters=self.mel_filters					,       log_mel='log10'					,       )
								A__	  =     log_spec[:, :-1]
								A__	  =     np.maximum(_snake_case					,       log_spec.max() - 8.0       )
								A__	  =     (log_spec + 4.0) / 4.0
								return log_spec



					@staticmethod
					# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
					def      _a				(  _snake_case       :							List[np.ndarray]					,       _snake_case       :							List[np.ndarray]					,       _snake_case       :							float = 0.0       ):






								"""simple docstring"""


								if attention_mask is not None:
											A__	  =     np.array(_snake_case					,       np.intaa       )
											A__	  =     []

											for vector, length in zip(_snake_case					,       attention_mask.sum(-1       )       ):
														A__	  =     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7       )
														if length < normed_slice.shape[0]:
																	A__	  =     padding_value

														normed_input_values.append(_snake_case       )
								else:
											A__	  =     [(x - x.mean()) / np.sqrt(x.var() + 1E-7       ) for x in input_values]

								return normed_input_values



					def __call__(  self       :							str					,       _snake_case       :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,       _snake_case       :							bool = True					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							Optional[Union[str, TensorType]] = None					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							Optional[str] = "max_length"					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							Optional[bool] = None					,       **_snake_case       :							Optional[int]					,       ):






								"""simple docstring"""


								if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
														raise ValueError(
														    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
														    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
														    F''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''       )
								else:
											logger.warning(
											    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
											    'Failing to do so can result in silent errors that might be hard to debug.'       )

								A__	  =     isinstance(_snake_case					,       np.ndarray       ) and len(raw_speech.shape       ) > 1
								if is_batched_numpy and len(raw_speech.shape       ) > 2:
											raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''       )
								A__	  =     is_batched_numpy or (
								    isinstance(_snake_case					,       (list, tuple)       ) and (isinstance(raw_speech[0]					,       (np.ndarray, tuple, list)       ))
								)

								if is_batched:
											A__	  =     [np.asarray([speech]					,       dtype=np.floataa       ).T for speech in raw_speech]
								elif not is_batched and not isinstance(_snake_case					,       np.ndarray       ):
											A__	  =     np.asarray(_snake_case					,       dtype=np.floataa       )
								elif isinstance(_snake_case					,       np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
											A__	  =     raw_speech.astype(np.floataa       )

								# always return batch
								if not is_batched:
											A__	  =     [np.asarray([raw_speech]       ).T]

								A__	  =     BatchFeature({'input_features': raw_speech}       )

								# convert into correct format for padding

								A__	  =     self.pad(
								    _snake_case					,       padding=_snake_case					,       max_length=max_length if max_length else self.n_samples					,       truncation=_snake_case					,       pad_to_multiple_of=_snake_case					,       return_attention_mask=return_attention_mask or do_normalize					,       )

								# zero-mean and unit-variance normalization
								if do_normalize:
											A__	  =     self.zero_mean_unit_var_norm(
											    padded_inputs['input_features']					,       attention_mask=padded_inputs['attention_mask']					,       padding_value=self.padding_value					,       )
											A__	  =     np.stack(padded_inputs['input_features']					,       axis=0       )

								# make sure list is in array format
								A__	  =     padded_inputs.get('input_features'       ).transpose(2					,       0					,       1       )

								A__	  =     [self._np_extract_fbank_features(_snake_case       ) for waveform in input_features[0]]

								if isinstance(input_features[0]					,       _snake_case       ):
											A__	  =     [np.asarray(_snake_case					,       dtype=np.floataa       ) for feature in input_features]
								else:
											A__	  =     input_features

								if return_attention_mask:
											# rescale from sample (48000) to feature (3000)
											A__	  =     padded_inputs['attention_mask'][:, :: self.hop_length]

								if return_tensors is not None:
											A__	  =     padded_inputs.convert_to_tensors(_snake_case       )

								return padded_inputs



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     copy.deepcopy(self.__dict__       )
								A__	  =     self.__class__.__name__
								if "mel_filters" in output:
											del output["mel_filters"]
								return output









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
							)




