


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






def   _lowercase(					__a				:						List[Any]     ):
							if "cls_token" in name:
														a__      =name.replace('cls_token'    ,     'vit.embeddings.cls_token'     )
							if "mask_token" in name:
														a__      =name.replace('mask_token'    ,     'decoder.mask_token'     )
							if "decoder_pos_embed" in name:
														a__      =name.replace('decoder_pos_embed'    ,     'decoder.decoder_pos_embed'     )
							if "pos_embed" in name and "decoder" not in name:
														a__      =name.replace('pos_embed'    ,     'vit.embeddings.position_embeddings'     )
							if "patch_embed.proj" in name:
														a__      =name.replace('patch_embed.proj'    ,     'vit.embeddings.patch_embeddings.projection'     )
							if "patch_embed.norm" in name:
														a__      =name.replace('patch_embed.norm'    ,     'vit.embeddings.norm'     )
							if "decoder_blocks" in name:
														a__      =name.replace('decoder_blocks'    ,     'decoder.decoder_layers'     )
							if "blocks" in name:
														a__      =name.replace('blocks'    ,     'vit.encoder.layer'     )
							if "attn.proj" in name:
														a__      =name.replace('attn.proj'    ,     'attention.output.dense'     )
							if "attn" in name:
														a__      =name.replace('attn'    ,     'attention.self'     )
							if "norm1" in name:
														a__      =name.replace('norm1'    ,     'layernorm_before'     )
							if "norm2" in name:
														a__      =name.replace('norm2'    ,     'layernorm_after'     )
							if "mlp.fc1" in name:
														a__      =name.replace('mlp.fc1'    ,     'intermediate.dense'     )
							if "mlp.fc2" in name:
														a__      =name.replace('mlp.fc2'    ,     'output.dense'     )
							if "decoder_embed" in name:
														a__      =name.replace('decoder_embed'    ,     'decoder.decoder_embed'     )
							if "decoder_norm" in name:
														a__      =name.replace('decoder_norm'    ,     'decoder.decoder_norm'     )
							if "decoder_pred" in name:
														a__      =name.replace('decoder_pred'    ,     'decoder.decoder_pred'     )
							if "norm.weight" in name and "decoder" not in name:
														a__      =name.replace('norm.weight'    ,     'vit.layernorm.weight'     )
							if "norm.bias" in name and "decoder" not in name:
														a__      =name.replace('norm.bias'    ,     'vit.layernorm.bias'     )

							return name






def   _lowercase(					__a				:						Any    ,     __a				:						Any     ):
							for key in orig_state_dict.copy().keys():
														a__      =orig_state_dict.pop(__a     )

														if "qkv" in key:
																					a__      =key.split('.'     )
																					a__      =int(key_split[1]     )
																					if "decoder_blocks" in key:
																												a__      =config.decoder_hidden_size
																												a__      ='decoder.decoder_layers.'
																												if "weight" in key:
																																			a__      =val[:dim, :]
																																			a__      =val[dim : dim * 2, :]
																																			a__      =val[-dim:, :]
																												elif "bias" in key:
																																			a__      =val[:dim]
																																			a__      =val[dim : dim * 2]
																																			a__      =val[-dim:]
																					else:
																												a__      =config.hidden_size
																												a__      ='vit.encoder.layer.'
																												if "weight" in key:
																																			a__      =val[:dim, :]
																																			a__      =val[dim : dim * 2, :]
																																			a__      =val[-dim:, :]
																												elif "bias" in key:
																																			a__      =val[:dim]
																																			a__      =val[dim : dim * 2]
																																			a__      =val[-dim:]

														else:
																					a__      =val

							return orig_state_dict






def   _lowercase(					__a				:						Any    ,     __a				:						Optional[Any]     ):
							a__      =ViTMAEConfig()
							if "large" in checkpoint_url:
														a__      =1024
														a__      =4096
														a__      =24
														a__      =16
							elif "huge" in checkpoint_url:
														a__      =14
														a__      =1280
														a__      =5120
														a__      =32
														a__      =16

							a__      =ViTMAEForPreTraining(__a     )

							a__      =torch.hub.load_state_dict_from_url(__a    ,     map_location='cpu'     )['model']

							a__      =ViTMAEImageProcessor(size=config.image_size     )

							a__      =convert_state_dict(__a    ,     __a     )

							model.load_state_dict(__a     )
							model.eval()

							a__      ='https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

							a__      =Image.open(requests.get(__a    ,     stream=__a     ).raw     )
							a__      =ViTMAEImageProcessor(size=config.image_size     )
							a__      =image_processor(images=__a    ,     return_tensors='pt'     )

							# forward pass
							torch.manual_seed(2     )
							a__      =model(**__a     )
							a__      =outputs.logits

							if "large" in checkpoint_url:
														a__      =torch.tensor(
														    [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]     )
							elif "huge" in checkpoint_url:
														a__      =torch.tensor(
														    [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]     )
							else:
														a__      =torch.tensor(
														    [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]     )

							# verify logits
							assert torch.allclose(logits[0, :3, :3]    ,     __a    ,     atol=1e-4     )

							print(f"""Saving model to {pytorch_dump_folder_path}"""     )
							model.save_pretrained(__a     )

							print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
							image_processor.save_pretrained(__a     )


if __name__ == "__main__":
	_lowerCAmelCase:						Dict   				=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--checkpoint_url',
	    default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
	    type=str,
	    help='URL of the checkpoint you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)

	_lowerCAmelCase:						str   				=       parser.parse_args()
	convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








"""simple docstring"""





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				bytes     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =F'{sampling_rate}'
							__lowerCamelCase   :   Union[str, Any]       ='''1'''
							__lowerCamelCase   :   List[Any]       ='''f32le'''
							__lowerCamelCase   :   Optional[int]       =[
							    '''ffmpeg''',
							    '''-i''',
							    '''pipe:0''',
							    '''-ac''',
							    ac,
							    '''-ar''',
							    ar,
							    '''-f''',
							    format_for_conversion,
							    '''-hide_banner''',
							    '''-loglevel''',
							    '''quiet''',
							    '''pipe:1''',
							]

							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,    stdin=subprocess.PIPE     ,    stdout=subprocess.PIPE ) as ffmpeg_process:
																					__lowerCamelCase   :   Union[str, Any]       =ffmpeg_process.communicate(SCREAMING_SNAKE_CASE )
							except FileNotFoundError as error:
														raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
							__lowerCamelCase   :   Any       =output_stream[0]
							__lowerCamelCase   :   Tuple       =np.frombuffer(SCREAMING_SNAKE_CASE     ,    np.floataa )
							if audio.shape[0] == 0:
														raise ValueError('''Malformed soundfile''' )
							return audio



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =F'{sampling_rate}'
							__lowerCamelCase   :   Union[str, Any]       ='''1'''
							if format_for_conversion == "s16le":
														__lowerCamelCase   :   List[Any]       =2
							elif format_for_conversion == "f32le":
														__lowerCamelCase   :   List[Any]       =4
							else:
														raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

							__lowerCamelCase   :   Union[str, Any]       =platform.system()
							if system == "Linux":
														__lowerCamelCase   :   Tuple       ='''alsa'''
														__lowerCamelCase   :   int       ='''default'''
							elif system == "Darwin":
														__lowerCamelCase   :   Any       ='''avfoundation'''
														__lowerCamelCase   :   List[str]       =''':0'''
							elif system == "Windows":
														__lowerCamelCase   :   Union[str, Any]       ='''dshow'''
														__lowerCamelCase   :   Dict       ='''default'''

							__lowerCamelCase   :   Any       =[
							    '''ffmpeg''',
							    '''-f''',
							    format_,
							    '''-i''',
							    input_,
							    '''-ac''',
							    ac,
							    '''-ar''',
							    ar,
							    '''-f''',
							    format_for_conversion,
							    '''-fflags''',
							    '''nobuffer''',
							    '''-hide_banner''',
							    '''-loglevel''',
							    '''quiet''',
							    '''pipe:1''',
							]
							__lowerCamelCase   :   Tuple       =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
							__lowerCamelCase   :   int       =_ffmpeg_stream(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
							for item in iterator:
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				Optional[int] = None     ,    SCREAMING_SNAKE_CASE							:				Optional[Union[Tuple[float, float], float]] = None     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







							if stream_chunk_s is not None:
														__lowerCamelCase   :   Optional[int]       =stream_chunk_s
							else:
														__lowerCamelCase   :   Tuple       =chunk_length_s

							__lowerCamelCase   :   Union[str, Any]       =ffmpeg_microphone(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    format_for_conversion=SCREAMING_SNAKE_CASE )
							if format_for_conversion == "s16le":
														__lowerCamelCase   :   int       =np.intaa
														__lowerCamelCase   :   Optional[int]       =2
							elif format_for_conversion == "f32le":
														__lowerCamelCase   :   Dict       =np.floataa
														__lowerCamelCase   :   Tuple       =4
							else:
														raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

							if stride_length_s is None:
														__lowerCamelCase   :   Tuple       =chunk_length_s / 6
							__lowerCamelCase   :   str       =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
							if isinstance(SCREAMING_SNAKE_CASE     ,    (int, float) ):
														__lowerCamelCase   :   List[str]       =[stride_length_s, stride_length_s]

							__lowerCamelCase   :   List[str]       =int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
							__lowerCamelCase   :   int       =int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
							__lowerCamelCase   :   Optional[int]       =datetime.datetime.now()
							__lowerCamelCase   :   List[Any]       =datetime.timedelta(seconds=SCREAMING_SNAKE_CASE )
							for item in chunk_bytes_iter(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    stride=(stride_left, stride_right)     ,    stream=SCREAMING_SNAKE_CASE ):
														# Put everything back in numpy scale
														__lowerCamelCase   :   Optional[int]       =np.frombuffer(item['''raw''']     ,    dtype=SCREAMING_SNAKE_CASE )
														__lowerCamelCase   :   Dict       =(
														    item['''stride'''][0] // size_of_sample,
														    item['''stride'''][1] // size_of_sample,
														)
														__lowerCamelCase   :   Optional[Any]       =sampling_rate
														audio_time += delta
														if datetime.datetime.now() > audio_time + 10 * delta:
																					# We're late !! SKIP
																					continue
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				Tuple[int, int]     ,    SCREAMING_SNAKE_CASE							:				bool = False ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[Any]       =B''''''
							__lowerCamelCase     ,    __lowerCamelCase   :   Dict       =stride
							if stride_left + stride_right >= chunk_len:
														raise ValueError(
														    F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}' )
							__lowerCamelCase   :   List[str]       =0
							for raw in iterator:
														acc += raw
														if stream and len(SCREAMING_SNAKE_CASE ) < chunk_len:
																					__lowerCamelCase   :   List[str]       =(_stride_left, 0)
																					yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
														else:
																					while len(SCREAMING_SNAKE_CASE ) >= chunk_len:
																												# We are flushing the accumulator
																												__lowerCamelCase   :   Optional[Any]       =(_stride_left, stride_right)
																												__lowerCamelCase   :   str       ={'''raw''': acc[:chunk_len], '''stride''': stride}
																												if stream:
																																			__lowerCamelCase   :   Optional[Any]       =False
																												yield item
																												__lowerCamelCase   :   Dict       =stride_left
																												__lowerCamelCase   :   str       =acc[chunk_len - stride_left - stride_right :]
    # Last chunk
							if len(SCREAMING_SNAKE_CASE ) > stride_left:
														__lowerCamelCase   :   List[str]       ={'''raw''': acc, '''stride''': (_stride_left, 0)}
														if stream:
																					__lowerCamelCase   :   Tuple       =False
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







							__lowerCamelCase   :   Dict       =2**24  # 16Mo
							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,    stdout=subprocess.PIPE     ,    bufsize=SCREAMING_SNAKE_CASE ) as ffmpeg_process:
																					while True:
																												__lowerCamelCase   :   Dict       =ffmpeg_process.stdout.read(SCREAMING_SNAKE_CASE )
																												if raw == b"":
																																			break
																												yield raw
							except FileNotFoundError as error:
														raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error




