





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						(				lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							[]
			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						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  str				):
			__lowercase										=							[]
			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						(				lowerCamelCase_			:  Dict				):
			__lowercase										=							[]
			token.append((f"cvt.encoder.stages.{idx}.cls_token", '''stage2.cls_token''')				)
			return token




def    _lowerCAmelCase						(				):
			__lowercase										=							[]
			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						(				lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Optional[Any]				):
			__lowercase										=							'''imagenet-1k-id2label.json'''
			__lowercase										=							1_0_0_0

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

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

			__lowercase										=							__lowercase										=							CvtConfig(num_labels=lowerCamelCase_		,       idalabel=lowerCamelCase_		,       labelaid=lowerCamelCase_				)

			# For depth size 13 (13 = 1+2+10)
			if cvt_model.rsplit('''/'''		,       1				)[-1][4:6] == "13":
						__lowercase										=							[1, 2, 1_0]

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

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

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

			__lowercase										=							OrderedDict()
			__lowercase										=							[]

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

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

			model.load_state_dict(lowerCamelCase_				)
			model.save_pretrained(lowerCamelCase_				)
			image_processor.save_pretrained(lowerCamelCase_				)


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

if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    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=3_8_4,
	    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.'''
	)

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












'''simple docstring'''



import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  List[str]				):
			__lowercase										=							UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''projector.weight''']
			__lowercase										=							downstream_dict['''projector.bias''']
			__lowercase										=							downstream_dict['''model.post_net.linear.weight''']
			__lowercase										=							downstream_dict['''model.post_net.linear.bias''']
			return model




def    _lowerCAmelCase						(				lowerCamelCase_			:  str		,       lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  Optional[Any]				):
			__lowercase										=							UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''model.linear.weight''']
			__lowercase										=							downstream_dict['''model.linear.bias''']
			return model




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							UniSpeechSatForXVector.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''connector.weight''']
			__lowercase										=							downstream_dict['''connector.bias''']
			for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
						__lowercase										=							downstream_dict[
						    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
						]
						__lowercase										=							downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
			__lowercase										=							downstream_dict['''objective.W''']
			return model




@torch.no_grad()
def    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)

			__lowercase										=							checkpoint['''Downstream''']

			__lowercase										=							UniSpeechSatConfig.from_pretrained(lowerCamelCase_				)
			__lowercase										=							WavaVecaFeatureExtractor.from_pretrained(
			    lowerCamelCase_		,       return_attention_mask=lowerCamelCase_		,       do_normalize=lowerCamelCase_				)

			__lowercase										=							hf_config.architectures[0]
			if arch.endswith('''ForSequenceClassification'''				):
						__lowercase										=							convert_classification(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			elif arch.endswith('''ForAudioFrameClassification'''				):
						__lowercase										=							convert_diarization(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			elif arch.endswith('''ForXVector'''				):
						__lowercase										=							convert_xvector(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			else:
						raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}"				)

			if hf_config.use_weighted_layer_sum:
						__lowercase										=							checkpoint['''Featurizer''']['''weights''']

			hf_feature_extractor.save_pretrained(lowerCamelCase_				)
			hf_model.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    argparse.ArgumentParser()
	parser.add_argument(
	    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
	)
	parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
	parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
	parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





