



"""simple docstring"""





from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			int					, __UpperCamelCase      :			float = 1 / sqrt(2    )    ):


						'''simple docstring'''






						snake_case_   : int						=			tau * frequency / samplerate
						snake_case_   : Any						=			sin(__UpperCamelCase    )
						snake_case_   : str						=			cos(__UpperCamelCase    )
						snake_case_   : List[Any]						=			_sin / (2 * q_factor)

						snake_case_   : List[str]						=			(1 - _cos) / 2
						snake_case_   : str						=			1 - _cos

						snake_case_   : Union[str, Any]						=			1 + alpha
						snake_case_   : Tuple						=			-2 * _cos
						snake_case_   : int						=			1 - alpha

						snake_case_   : List[str]						=			IIRFilter(2    )
						filt.set_coefficients([aa, aa, aa]					, [ba, ba, ba]    )
						return filt
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			int					, __UpperCamelCase      :			float = 1 / sqrt(2    )    ):


						'''simple docstring'''






						snake_case_   : Any						=			tau * frequency / samplerate
						snake_case_   : Union[str, Any]						=			sin(__UpperCamelCase    )
						snake_case_   : Tuple						=			cos(__UpperCamelCase    )
						snake_case_   : int						=			_sin / (2 * q_factor)

						snake_case_   : str						=			(1 + _cos) / 2
						snake_case_   : Union[str, Any]						=			-1 - _cos

						snake_case_   : List[Any]						=			1 + alpha
						snake_case_   : str						=			-2 * _cos
						snake_case_   : Optional[int]						=			1 - alpha

						snake_case_   : int						=			IIRFilter(2    )
						filt.set_coefficients([aa, aa, aa]					, [ba, ba, ba]    )
						return filt
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			int					, __UpperCamelCase      :			float = 1 / sqrt(2    )    ):


						'''simple docstring'''






						snake_case_   : str						=			tau * frequency / samplerate
						snake_case_   : Union[str, Any]						=			sin(__UpperCamelCase    )
						snake_case_   : Any						=			cos(__UpperCamelCase    )
						snake_case_   : Optional[Any]						=			_sin / (2 * q_factor)

						snake_case_   : str						=			_sin / 2
						snake_case_   : List[Any]						=			0
						snake_case_   : Any						=			-ba

						snake_case_   : Optional[int]						=			1 + alpha
						snake_case_   : Union[str, Any]						=			-2 * _cos
						snake_case_   : Any						=			1 - alpha

						snake_case_   : str						=			IIRFilter(2    )
						filt.set_coefficients([aa, aa, aa]					, [ba, ba, ba]    )
						return filt
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			int					, __UpperCamelCase      :			float = 1 / sqrt(2    )    ):


						'''simple docstring'''






						snake_case_   : int						=			tau * frequency / samplerate
						snake_case_   : str						=			sin(__UpperCamelCase    )
						snake_case_   : Optional[int]						=			cos(__UpperCamelCase    )
						snake_case_   : Any						=			_sin / (2 * q_factor)

						snake_case_   : List[str]						=			1 - alpha
						snake_case_   : Optional[Any]						=			-2 * _cos
						snake_case_   : List[Any]						=			1 + alpha

						snake_case_   : int						=			IIRFilter(2    )
						filt.set_coefficients([ba, ba, ba]					, [ba, ba, ba]    )
						return filt
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			int					, __UpperCamelCase      :			float					, __UpperCamelCase      :			float = 1 / sqrt(2    )					, ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			tau * frequency / samplerate
						snake_case_   : Optional[Any]						=			sin(__UpperCamelCase    )
						snake_case_   : Tuple						=			cos(__UpperCamelCase    )
						snake_case_   : Any						=			_sin / (2 * q_factor)
						snake_case_   : Any						=			1_0 ** (gain_db / 4_0)

						snake_case_   : Optional[Any]						=			1 + alpha * big_a
						snake_case_   : Any						=			-2 * _cos
						snake_case_   : Any						=			1 - alpha * big_a
						snake_case_   : Union[str, Any]						=			1 + alpha / big_a
						snake_case_   : List[str]						=			-2 * _cos
						snake_case_   : int						=			1 - alpha / big_a

						snake_case_   : str						=			IIRFilter(2    )
						filt.set_coefficients([aa, aa, aa]					, [ba, ba, ba]    )
						return filt
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			int					, __UpperCamelCase      :			float					, __UpperCamelCase      :			float = 1 / sqrt(2    )					, ):


						'''simple docstring'''






						snake_case_   : List[Any]						=			tau * frequency / samplerate
						snake_case_   : List[str]						=			sin(__UpperCamelCase    )
						snake_case_   : Optional[int]						=			cos(__UpperCamelCase    )
						snake_case_   : int						=			_sin / (2 * q_factor)
						snake_case_   : int						=			1_0 ** (gain_db / 4_0)
						snake_case_   : Tuple						=			(big_a + 1) - (big_a - 1) * _cos
						snake_case_   : int						=			(big_a + 1) + (big_a - 1) * _cos
						snake_case_   : Any						=			(big_a - 1) - (big_a + 1) * _cos
						snake_case_   : Union[str, Any]						=			(big_a - 1) + (big_a + 1) * _cos
						snake_case_   : List[str]						=			2 * sqrt(__UpperCamelCase    ) * alpha

						snake_case_   : Optional[int]						=			big_a * (pmc + aaa)
						snake_case_   : Optional[Any]						=			2 * big_a * mpc
						snake_case_   : Union[str, Any]						=			big_a * (pmc - aaa)
						snake_case_   : Dict						=			ppmc + aaa
						snake_case_   : str						=			-2 * pmpc
						snake_case_   : str						=			ppmc - aaa

						snake_case_   : List[str]						=			IIRFilter(2    )
						filt.set_coefficients([aa, aa, aa]					, [ba, ba, ba]    )
						return filt






def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			int					, __UpperCamelCase      :			float					, __UpperCamelCase      :			float = 1 / sqrt(2    )					, ):


						'''simple docstring'''






						snake_case_   : Dict						=			tau * frequency / samplerate
						snake_case_   : int						=			sin(__UpperCamelCase    )
						snake_case_   : List[Any]						=			cos(__UpperCamelCase    )
						snake_case_   : int						=			_sin / (2 * q_factor)
						snake_case_   : Dict						=			1_0 ** (gain_db / 4_0)
						snake_case_   : Dict						=			(big_a + 1) - (big_a - 1) * _cos
						snake_case_   : Any						=			(big_a + 1) + (big_a - 1) * _cos
						snake_case_   : Any						=			(big_a - 1) - (big_a + 1) * _cos
						snake_case_   : List[str]						=			(big_a - 1) + (big_a + 1) * _cos
						snake_case_   : int						=			2 * sqrt(__UpperCamelCase    ) * alpha

						snake_case_   : Optional[Any]						=			big_a * (ppmc + aaa)
						snake_case_   : List[str]						=			-2 * big_a * pmpc
						snake_case_   : Any						=			big_a * (ppmc - aaa)
						snake_case_   : Optional[Any]						=			pmc + aaa
						snake_case_   : Optional[Any]						=			2 * mpc
						snake_case_   : str						=			pmc - aaa

						snake_case_   : Any						=			IIRFilter(2    )
						filt.set_coefficients([aa, aa, aa]					, [ba, ba, ba]    )
						return filt






"""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 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : List[str]						=			[]
						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 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : str						=			[]
						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 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						snake_case_   : int						=			[]
						token.append((F'cvt.encoder.stages.{idx}.cls_token', """stage2.cls_token""")    )
						return token
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			[]
						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 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			"""imagenet-1k-id2label.json"""
						snake_case_   : Optional[Any]						=			1_0_0_0

						snake_case_   : Any						=			"""huggingface/label-files"""
						snake_case_   : Tuple						=			num_labels
						snake_case_   : Dict						=			json.load(open(cached_download(hf_hub_url(__UpperCamelCase					, __UpperCamelCase					, repo_type="""dataset"""    )    )					, """r"""    )    )
						snake_case_   : str						=			{int(__UpperCamelCase    ): v for k, v in idalabel.items()}

						snake_case_   : List[str]						=			idalabel
						snake_case_   : Any						=			{v: k for k, v in idalabel.items()}

						snake_case_   : Dict						=			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_   : Any						=			[1, 2, 1_0]

						# For depth size 21 (21 = 1+4+16)
						elif cvt_model.rsplit("""/"""					, 1    )[-1][4:6] == "21":
												snake_case_   : Any						=			[1, 4, 1_6]

						# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
						else:
												snake_case_   : Optional[int]						=			[2, 2, 2_0]
												snake_case_   : str						=			[3, 1_2, 1_6]
												snake_case_   : Any						=			[1_9_2, 7_6_8, 1_0_2_4]

						snake_case_   : Union[str, Any]						=			CvtForImageClassification(__UpperCamelCase    )
						snake_case_   : str						=			AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""    )
						snake_case_   : List[Any]						=			image_size
						snake_case_   : str						=			torch.load(__UpperCamelCase					, map_location=torch.device("""cpu"""    )    )

						snake_case_   : Any						=			OrderedDict()
						snake_case_   : Tuple						=			[]

						for idx in range(len(config.depth    )    ):
												if config.cls_token[idx]:
																		snake_case_   : Optional[Any]						=			list_of_state_dict + cls_token(__UpperCamelCase    )
												snake_case_   : str						=			list_of_state_dict + embeddings(__UpperCamelCase    )
												for cnt in range(config.depth[idx]    ):
																		snake_case_   : List[str]						=			list_of_state_dict + attention(__UpperCamelCase					, __UpperCamelCase    )

						snake_case_   : str						=			list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(__UpperCamelCase    )
						for i in range(len(__UpperCamelCase    )    ):
												snake_case_   : Union[str, Any]						=			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__":
					__lowerCAmelCase       :					Optional[int]      					=					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.'''
					)

					__lowerCAmelCase       :					Dict      					=					parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)

