"""simple docstring"""


import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      Dict:
     snake_case_					       =					[]
     embed.append(
         (
             f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
             f'stage{idx}.patch_embed.proj.weight',
         )					)
     embed.append(
         (
             f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
             f'stage{idx}.patch_embed.proj.bias',
         )					)
     embed.append(
         (
             f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
             f'stage{idx}.patch_embed.norm.weight',
         )					)
     embed.append(
         (
             f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
             f'stage{idx}.patch_embed.norm.bias',
         )					)
     return embed







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase					)				->      Union[str, Any]:
     snake_case_					       =					[]
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
             f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
             f'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
             f'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
             f'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
             f'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
             f'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
             f'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
             f'stage{idx}.blocks.{cnt}.attn.proj.weight',
         )					)
     attention_weights.append(
         (
             f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
             f'stage{idx}.blocks.{cnt}.attn.proj.bias',
         )					)
     attention_weights.append(
         (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc1.weight')					)
     attention_weights.append(
         (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc1.bias')					)
     attention_weights.append(
         (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc2.weight')					)
     attention_weights.append(
         (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc2.bias')					)
     attention_weights.append(
         (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', f'stage{idx}.blocks.{cnt}.norm1.weight')					)
     attention_weights.append(
         (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', f'stage{idx}.blocks.{cnt}.norm1.bias')					)
     attention_weights.append(
         (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', f'stage{idx}.blocks.{cnt}.norm2.weight')					)
     attention_weights.append(
         (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', f'stage{idx}.blocks.{cnt}.norm2.bias')					)
     return attention_weights







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      Dict:
     snake_case_					       =					[]
     token.append((f'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token')					)
     return token







def 					UpperCAmelCase    (		)				->      Optional[Any]:
     snake_case_					       =					[]
     head.append(('layernorm.weight', 'norm.weight')					)
     head.append(('layernorm.bias', 'norm.bias')					)
     head.append(('classifier.weight', 'head.weight')					)
     head.append(('classifier.bias', 'head.bias')					)
     return head







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)				->      Optional[Any]:
     snake_case_					       =					'imagenet-1k-id2label.json'
     snake_case_					       =					1000

     snake_case_					       =					'huggingface/label-files'
     snake_case_					       =					num_labels
     snake_case_					       =					json.load(open(cached_download(hf_hub_url(UpperCAmelCase						,					UpperCAmelCase						,					repo_type='dataset'					)					)						,					'r'					)					)
     snake_case_					       =					{int(UpperCAmelCase					): v for k, v in idalabel.items()}

     snake_case_					       =					idalabel
     snake_case_					       =					{v: k for k, v in idalabel.items()}

     snake_case_					       =					snake_case_					       =					CvtConfig(num_labels=UpperCAmelCase						,					idalabel=UpperCAmelCase						,					labelaid=UpperCAmelCase					)

     # For depth size 13 (13 = 1+2+10)
     if cvt_model.rsplit('/'						,					1					)[-1][4:6] == "13":
          snake_case_					       =					[1, 2, 10]

     # For depth size 21 (21 = 1+4+16)
     elif cvt_model.rsplit('/'						,					1					)[-1][4:6] == "21":
          snake_case_					       =					[1, 4, 16]

     # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
     else:
          snake_case_					       =					[2, 2, 20]
          snake_case_					       =					[3, 12, 16]
          snake_case_					       =					[192, 768, 1024]

     snake_case_					       =					CvtForImageClassification(UpperCAmelCase					)
     snake_case_					       =					AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'					)
     snake_case_					       =					image_size
     snake_case_					       =					torch.load(UpperCAmelCase						,					map_location=torch.device('cpu'					)					)

     snake_case_					       =					OrderedDict()
     snake_case_					       =					[]

     for idx in range(len(config.depth					)					):
          if config.cls_token[idx]:
               snake_case_					       =					list_of_state_dict + cls_token(UpperCAmelCase					)
          snake_case_					       =					list_of_state_dict + embeddings(UpperCAmelCase					)
          for cnt in range(config.depth[idx]					):
               snake_case_					       =					list_of_state_dict + attention(UpperCAmelCase						,					UpperCAmelCase					)

     snake_case_					       =					list_of_state_dict + final()
     for gg in list_of_state_dict:
          print(UpperCAmelCase					)
     for i in range(len(UpperCAmelCase					)					):
          snake_case_					       =					original_weights[list_of_state_dict[i][1]]

     model.load_state_dict(UpperCAmelCase					)
     model.save_pretrained(UpperCAmelCase					)
     image_processor.save_pretrained(UpperCAmelCase					)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
    __UpperCamelCase									=			argparse.ArgumentParser()
    parser.add_argument(
        '''--cvt_model''',
        default='''cvt-w24''',
        type=str,
        help='''Name of the cvt model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--image_size''',
        default=384,
        type=int,
        help='''Input Image Size''',
    )
    parser.add_argument(
        '''--cvt_file_name''',
        default=r'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
        type=str,
        help='''Input Image Size''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    __UpperCamelCase									=			parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)







"""simple docstring"""






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

import numpy as np







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Tuple    =    f'''{sampling_rate}'''
				_lowerCamelCase     :			str    =    '1'
				_lowerCamelCase     :			str    =    'f32le'
				_lowerCamelCase     :			Union[str, Any]    =    [
				    'ffmpeg',
				    '-i',
				    'pipe:0',
				    '-ac',
				    ac,
				    '-ar',
				    ar,
				    '-f',
				    format_for_conversion,
				    '-hide_banner',
				    '-loglevel',
				    'quiet',
				    'pipe:1',
				]

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







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__ = "f32le"							,    ):
				_lowerCamelCase     :			Optional[Any]    =    f'''{sampling_rate}'''
				_lowerCamelCase     :			List[str]    =    '1'
				if format_for_conversion == "s16le":
								_lowerCamelCase     :			List[str]    =    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     :			Dict    =    platform.system()
				if system == "Linux":
								_lowerCamelCase     :			Optional[int]    =    'alsa'
								_lowerCamelCase     :			Optional[Any]    =    'default'
				elif system == "Darwin":
								_lowerCamelCase     :			Optional[int]    =    'avfoundation'
								_lowerCamelCase     :			Any    =    ':0'
				elif system == "Windows":
								_lowerCamelCase     :			Tuple    =    'dshow'
								_lowerCamelCase     :			Tuple    =    'default'

				_lowerCamelCase     :			Optional[int]    =    [
				    '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     :			List[Any]    =    _ffmpeg_stream(lowercase__							,    lowercase__		)
				for item in iterator:
								yield item







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = "f32le"							,    ):
				if stream_chunk_s is not None:
								_lowerCamelCase     :			int    =    stream_chunk_s
				else:
								_lowerCamelCase     :			Optional[Any]    =    chunk_length_s

				_lowerCamelCase     :			Optional[Any]    =    ffmpeg_microphone(lowercase__							,    lowercase__							,    format_for_conversion=lowercase__		)
				if format_for_conversion == "s16le":
								_lowerCamelCase     :			List[str]    =    np.intaa
								_lowerCamelCase     :			str    =    2
				elif format_for_conversion == "f32le":
								_lowerCamelCase     :			Any    =    np.floataa
								_lowerCamelCase     :			List[Any]    =    4
				else:
								raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''		)

				if stride_length_s is None:
								_lowerCamelCase     :			Union[str, Any]    =    chunk_length_s / 6
				_lowerCamelCase     :			Optional[int]    =    int(round(sampling_rate * chunk_length_s		)		) * size_of_sample
				if isinstance(lowercase__							,    (int, float)		):
								_lowerCamelCase     :			Any    =    [stride_length_s, stride_length_s]

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







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = False		):
				_lowerCamelCase     :			int    =    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     :			str    =    0
				for raw in iterator:
								acc += raw
								if stream and len(lowercase__		) < chunk_len:
												_lowerCamelCase     :			Optional[int]    =    (_stride_left, 0)
												yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
								else:
												while len(lowercase__		) >= chunk_len:
																# We are flushing the accumulator
																_lowerCamelCase     :			str    =    (_stride_left, stride_right)
																_lowerCamelCase     :			str    =    {'raw': acc[:chunk_len], 'stride': stride}
																if stream:
																				_lowerCamelCase     :			List[Any]    =    False
																yield item
																_lowerCamelCase     :			Optional[Any]    =    stride_left
																_lowerCamelCase     :			str    =    acc[chunk_len - stride_left - stride_right :]
    # Last chunk
				if len(lowercase__		) > stride_left:
								_lowerCamelCase     :			Optional[Any]    =    {'raw': acc, 'stride': (_stride_left, 0)}
								if stream:
												_lowerCamelCase     :			Tuple    =    False
								yield item







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			int    =    2**24  # 16Mo
				try:
								with subprocess.Popen(lowercase__							,    stdout=subprocess.PIPE							,    bufsize=lowercase__		) as ffmpeg_process:
												while True:
																_lowerCamelCase     :			Optional[Any]    =    ffmpeg_process.stdout.read(lowercase__		)
																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