
'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification







def 	A  (   UpperCamelCase_       :  int   )       ->							Optional[Any]:







			'''simple docstring'''



			lowerCAmelCase__										=				SwinvaConfig()
			lowerCAmelCase__										=				swinva_name.split("_"   )

			lowerCAmelCase__										=				name_split[1]
			if "to" in name_split[3]:
						lowerCAmelCase__										=				int(name_split[3][-3:]   )
			else:
						lowerCAmelCase__										=				int(name_split[3]   )
			if "to" in name_split[2]:
						lowerCAmelCase__										=				int(name_split[2][-2:]   )
			else:
						lowerCAmelCase__										=				int(name_split[2][6:]   )

			if model_size == "tiny":
						lowerCAmelCase__										=				96
						lowerCAmelCase__										=				(2, 2, 6, 2)
						lowerCAmelCase__										=				(3, 6, 12, 24)
			elif model_size == "small":
						lowerCAmelCase__										=				96
						lowerCAmelCase__										=				(2, 2, 18, 2)
						lowerCAmelCase__										=				(3, 6, 12, 24)
			elif model_size == "base":
						lowerCAmelCase__										=				1_28
						lowerCAmelCase__										=				(2, 2, 18, 2)
						lowerCAmelCase__										=				(4, 8, 16, 32)
			else:
						lowerCAmelCase__										=				1_92
						lowerCAmelCase__										=				(2, 2, 18, 2)
						lowerCAmelCase__										=				(6, 12, 24, 48)

			if "to" in swinva_name:
						lowerCAmelCase__										=				(12, 12, 12, 6)

			if ("22k" in swinva_name) and ("to" not in swinva_name):
						lowerCAmelCase__										=				2_18_41
						lowerCAmelCase__										=				"huggingface/label-files"
						lowerCAmelCase__										=				"imagenet-22k-id2label.json"
						lowerCAmelCase__										=				json.load(open(hf_hub_download(UpperCamelCase_    ,						UpperCamelCase_    ,						repo_type="dataset"   )    ,						"r"   )   )
						lowerCAmelCase__										=				{int(UpperCamelCase_   ): v for k, v in idalabel.items()}
						lowerCAmelCase__										=				idalabel
						lowerCAmelCase__										=				{v: k for k, v in idalabel.items()}

			else:
						lowerCAmelCase__										=				10_00
						lowerCAmelCase__										=				"huggingface/label-files"
						lowerCAmelCase__										=				"imagenet-1k-id2label.json"
						lowerCAmelCase__										=				json.load(open(hf_hub_download(UpperCamelCase_    ,						UpperCamelCase_    ,						repo_type="dataset"   )    ,						"r"   )   )
						lowerCAmelCase__										=				{int(UpperCamelCase_   ): v for k, v in idalabel.items()}
						lowerCAmelCase__										=				idalabel
						lowerCAmelCase__										=				{v: k for k, v in idalabel.items()}

			lowerCAmelCase__										=				img_size
			lowerCAmelCase__										=				num_classes
			lowerCAmelCase__										=				embed_dim
			lowerCAmelCase__										=				depths
			lowerCAmelCase__										=				num_heads
			lowerCAmelCase__										=				window_size

			return config







def 	A  (   UpperCamelCase_       :  Dict   )       ->							int:







			'''simple docstring'''



			if "patch_embed.proj" in name:
						lowerCAmelCase__										=				name.replace("patch_embed.proj"    ,						"embeddings.patch_embeddings.projection"   )
			if "patch_embed.norm" in name:
						lowerCAmelCase__										=				name.replace("patch_embed.norm"    ,						"embeddings.norm"   )
			if "layers" in name:
						lowerCAmelCase__										=				"encoder." + name
			if "attn.proj" in name:
						lowerCAmelCase__										=				name.replace("attn.proj"    ,						"attention.output.dense"   )
			if "attn" in name:
						lowerCAmelCase__										=				name.replace("attn"    ,						"attention.self"   )
			if "norm1" in name:
						lowerCAmelCase__										=				name.replace("norm1"    ,						"layernorm_before"   )
			if "norm2" in name:
						lowerCAmelCase__										=				name.replace("norm2"    ,						"layernorm_after"   )
			if "mlp.fc1" in name:
						lowerCAmelCase__										=				name.replace("mlp.fc1"    ,						"intermediate.dense"   )
			if "mlp.fc2" in name:
						lowerCAmelCase__										=				name.replace("mlp.fc2"    ,						"output.dense"   )
			if "q_bias" in name:
						lowerCAmelCase__										=				name.replace("q_bias"    ,						"query.bias"   )
			if "k_bias" in name:
						lowerCAmelCase__										=				name.replace("k_bias"    ,						"key.bias"   )
			if "v_bias" in name:
						lowerCAmelCase__										=				name.replace("v_bias"    ,						"value.bias"   )
			if "cpb_mlp" in name:
						lowerCAmelCase__										=				name.replace("cpb_mlp"    ,						"continuous_position_bias_mlp"   )
			if name == "norm.weight":
						lowerCAmelCase__										=				"layernorm.weight"
			if name == "norm.bias":
						lowerCAmelCase__										=				"layernorm.bias"

			if "head" in name:
						lowerCAmelCase__										=				name.replace("head"    ,						"classifier"   )
			else:
						lowerCAmelCase__										=				"swinv2." + name

			return name







def 	A  (   UpperCamelCase_       :  List[Any]    ,						UpperCamelCase_       :  Optional[int]   )       ->							List[str]:







			'''simple docstring'''



			for key in orig_state_dict.copy().keys():
						lowerCAmelCase__										=				orig_state_dict.pop(UpperCamelCase_   )

						if "mask" in key:
									continue
						elif "qkv" in key:
									lowerCAmelCase__										=				key.split("."   )
									lowerCAmelCase__										=				int(key_split[1]   )
									lowerCAmelCase__										=				int(key_split[3]   )
									lowerCAmelCase__										=				model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

									if "weight" in key:
												lowerCAmelCase__										=				val[:dim, :]
												lowerCAmelCase__										=				val[dim : dim * 2, :]
												lowerCAmelCase__										=				val[-dim:, :]
									else:
												lowerCAmelCase__										=				val[:dim]
												lowerCAmelCase__										=				val[
												    dim : dim * 2
												]
												lowerCAmelCase__										=				val[-dim:]
						else:
									lowerCAmelCase__										=				val

			return orig_state_dict







def 	A  (   UpperCamelCase_       :  Optional[int]    ,						UpperCamelCase_       :  Any   )       ->							Optional[Any]:







			'''simple docstring'''



			lowerCAmelCase__										=				timm.create_model(UpperCamelCase_    ,						pretrained=UpperCamelCase_   )
			timm_model.eval()

			lowerCAmelCase__										=				get_swinva_config(UpperCamelCase_   )
			lowerCAmelCase__										=				SwinvaForImageClassification(UpperCamelCase_   )
			model.eval()

			lowerCAmelCase__										=				convert_state_dict(timm_model.state_dict()    ,						UpperCamelCase_   )
			model.load_state_dict(UpperCamelCase_   )

			lowerCAmelCase__										=				"http://images.cocodataset.org/val2017/000000039769.jpg"

			lowerCAmelCase__										=				AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"    ,						"-"   )   )   )
			lowerCAmelCase__										=				Image.open(requests.get(UpperCamelCase_    ,						stream=UpperCamelCase_   ).raw   )
			lowerCAmelCase__										=				image_processor(images=UpperCamelCase_    ,						return_tensors="pt"   )

			lowerCAmelCase__										=				timm_model(inputs["pixel_values"]   )
			lowerCAmelCase__										=				model(**UpperCamelCase_   ).logits

			assert torch.allclose(UpperCamelCase_    ,						UpperCamelCase_    ,						atol=1E-3   )

			print(F"""Saving model {swinva_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_   )

			model.push_to_hub(
			    repo_path_or_name=Path(UpperCamelCase_    ,						UpperCamelCase_   )    ,						organization="nandwalritik"    ,						commit_message="Add model"    ,						)


if __name__ == "__main__":
		UpperCAmelCase__ :			int											=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--swinv2_name",
		    default="swinv2_tiny_patch4_window8_256",
		    type=str,
		    help="Name of the Swinv2 timm model you'd like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
		)

		UpperCAmelCase__ :			Tuple											=		parser.parse_args()
		convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)












'''simple docstring'''

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
   from ...feature_extraction_utils import FeatureExtractionMixin
   from ...tokenization_utils_base import PreTrainedTokenizerBase
   from ...utils import TensorType

lowercase__		:     Optional[Any]			=							logging.get_logger(__name__)

lowercase__		:     Union[str, Any]			=							{
    'openai/whisper-base': 'https://huggingface.co/openai/whisper-base/resolve/main/config.json',
}

# fmt: off
lowercase__		:     Any			=							[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 3_57, 3_66, 4_38, 5_32, 6_85,
    7_05, 7_96, 9_30, 10_58, 12_20, 12_67, 12_79, 13_03, 13_43, 13_77,
    13_91, 16_35, 17_82, 18_75, 21_62, 23_61, 24_88, 34_67, 40_08, 42_11,
    46_00, 48_08, 52_99, 58_55, 63_29, 72_03, 96_09, 99_59, 1_05_63, 1_07_86,
    1_14_20, 1_17_09, 1_19_07, 1_31_63, 1_36_97, 1_37_00, 1_48_08, 1_53_06, 1_64_10, 1_67_91,
    1_79_92, 1_92_03, 1_95_10, 2_07_24, 2_23_05, 2_29_35, 2_70_07, 3_01_09, 3_04_20, 3_34_09,
    3_49_49, 4_02_83, 4_04_93, 4_05_49, 4_72_82, 4_91_46, 5_02_57, 5_03_59, 5_03_60, 5_03_61
]
lowercase__		:     str			=							[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 3_59, 5_03, 5_22, 5_42, 8_73,
    8_93, 9_02, 9_18, 9_22, 9_31, 13_50, 18_53, 19_82, 24_60, 26_27,
    32_46, 32_53, 32_68, 35_36, 38_46, 39_61, 41_83, 46_67, 65_85, 66_47,
    72_73, 90_61, 93_83, 1_04_28, 1_09_29, 1_19_38, 1_20_33, 1_23_31, 1_25_62, 1_37_93,
    1_41_57, 1_46_35, 1_52_65, 1_56_18, 1_65_53, 1_66_04, 1_83_62, 1_89_56, 2_00_75, 2_16_75,
    2_25_20, 2_61_30, 2_61_61, 2_64_35, 2_82_79, 2_94_64, 3_16_50, 3_23_02, 3_24_70, 3_68_65,
    4_28_63, 4_74_25, 4_98_70, 5_02_54, 5_02_58, 5_03_60, 5_03_61, 5_03_62
]
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[int]									=       'whisper'
       _snake_case      :	Union[str, Any]									=       ['past_key_values']
       _snake_case      :	Union[str, Any]									=       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





       def __init__(				self  :						Tuple			,					lowerCAmelCase__  :						List[str]=51865			,					lowerCAmelCase__  :						Optional[Any]=80			,					lowerCAmelCase__  :						Optional[int]=6			,					lowerCAmelCase__  :						List[Any]=4			,					lowerCAmelCase__  :						Any=6			,					lowerCAmelCase__  :						List[Any]=4			,					lowerCAmelCase__  :						List[Any]=1536			,					lowerCAmelCase__  :						Dict=1536			,					lowerCAmelCase__  :						int=0.0			,					lowerCAmelCase__  :						Optional[Any]=0.0			,					lowerCAmelCase__  :						int=50257			,					lowerCAmelCase__  :						Tuple=True			,					lowerCAmelCase__  :						Optional[Any]=True			,					lowerCAmelCase__  :						Optional[int]="gelu"			,					lowerCAmelCase__  :						Optional[int]=256			,					lowerCAmelCase__  :						Any=0.0			,					lowerCAmelCase__  :						Tuple=0.0			,					lowerCAmelCase__  :						Optional[Any]=0.0			,					lowerCAmelCase__  :						str=0.02			,					lowerCAmelCase__  :						Dict=False			,					lowerCAmelCase__  :						int=1500			,					lowerCAmelCase__  :						int=448			,					lowerCAmelCase__  :						Union[str, Any]=50256			,					lowerCAmelCase__  :						str=50256			,					lowerCAmelCase__  :						Optional[int]=50256			,					lowerCAmelCase__  :						List[str]=None			,					lowerCAmelCase__  :						Optional[int]=[220, 50256]			,					lowerCAmelCase__  :						List[Any]=False			,					lowerCAmelCase__  :						str=256			,					lowerCAmelCase__  :						Optional[Any]=False			,					lowerCAmelCase__  :						Any=0.05			,					lowerCAmelCase__  :						Optional[int]=10			,					lowerCAmelCase__  :						Dict=2			,					lowerCAmelCase__  :						int=0.0			,					lowerCAmelCase__  :						Dict=10			,					lowerCAmelCase__  :						List[str]=0			,					lowerCAmelCase__  :						List[Any]=7			,					**lowerCAmelCase__  :						List[str]			,					)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			vocab_size
            _UpperCamelCase							       =			num_mel_bins
            _UpperCamelCase							       =			d_model
            _UpperCamelCase							       =			encoder_layers
            _UpperCamelCase							       =			encoder_attention_heads
            _UpperCamelCase							       =			decoder_layers
            _UpperCamelCase							       =			decoder_attention_heads
            _UpperCamelCase							       =			decoder_ffn_dim
            _UpperCamelCase							       =			encoder_ffn_dim
            _UpperCamelCase							       =			dropout
            _UpperCamelCase							       =			attention_dropout
            _UpperCamelCase							       =			activation_dropout
            _UpperCamelCase							       =			activation_function
            _UpperCamelCase							       =			init_std
            _UpperCamelCase							       =			encoder_layerdrop
            _UpperCamelCase							       =			decoder_layerdrop
            _UpperCamelCase							       =			use_cache
            _UpperCamelCase							       =			encoder_layers
            _UpperCamelCase							       =			scale_embedding  # scale factor will be sqrt(d_model) if True
            _UpperCamelCase							       =			max_source_positions
            _UpperCamelCase							       =			max_target_positions

            # Audio Classification-specific parameters. Feel free to ignore for other classes.
            _UpperCamelCase							       =			classifier_proj_size
            _UpperCamelCase							       =			use_weighted_layer_sum

            # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
            _UpperCamelCase							       =			apply_spec_augment
            _UpperCamelCase							       =			mask_time_prob
            _UpperCamelCase							       =			mask_time_length
            _UpperCamelCase							       =			mask_time_min_masks
            _UpperCamelCase							       =			mask_feature_prob
            _UpperCamelCase							       =			mask_feature_length
            _UpperCamelCase							       =			mask_feature_min_masks

            _UpperCamelCase							       =			median_filter_width

            super().__init__(
                pad_token_id=lowerCAmelCase__			,					bos_token_id=lowerCAmelCase__			,					eos_token_id=lowerCAmelCase__			,					is_encoder_decoder=lowerCAmelCase__			,					decoder_start_token_id=lowerCAmelCase__			,					suppress_tokens=lowerCAmelCase__			,					begin_suppress_tokens=lowerCAmelCase__			,					**lowerCAmelCase__			,					)

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





       @property
       def snake_case__		(				self  :						Optional[Any]	)       ->	Mapping[str, Mapping[int, str]]:

            '''simple docstring'''




            _UpperCamelCase							       =			OrderedDict(
                [
                    ('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}),
                ]	)
            if self.use_past:
                 _UpperCamelCase							       =			{0: '''batch'''}
            else:
                 _UpperCamelCase							       =			{0: '''batch''', 1: '''decoder_sequence'''}

            if self.use_past:
                 self.fill_with_past_key_values_(lowerCAmelCase__			,					direction='''inputs'''	)

            return common_inputs





       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]			,					lowerCAmelCase__  :						int = -1			,					lowerCAmelCase__  :						int = -1			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						Optional["TensorType"] = None			,					lowerCAmelCase__  :						int = 22050			,					lowerCAmelCase__  :						float = 5.0			,					lowerCAmelCase__  :						int = 220			,					)       ->	Mapping[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			OrderedDict()
            _UpperCamelCase							       =			OnnxConfig.generate_dummy_inputs(
                self			,					preprocessor=preprocessor.feature_extractor			,					batch_size=lowerCAmelCase__			,					framework=lowerCAmelCase__			,					sampling_rate=lowerCAmelCase__			,					time_duration=lowerCAmelCase__			,					frequency=lowerCAmelCase__			,					)
            _UpperCamelCase							       =			encoder_inputs['''input_features'''].shape[2]
            _UpperCamelCase							       =			encoder_sequence_length // 2 if self.use_past else seq_length

            _UpperCamelCase							       =			super().generate_dummy_inputs(
                preprocessor.tokenizer			,					lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			encoder_inputs.pop('''input_features'''	)
            _UpperCamelCase							       =			decoder_inputs.pop('''decoder_input_ids'''	)

            if "past_key_values" in decoder_inputs:
                 _UpperCamelCase							       =			decoder_inputs.pop('''past_key_values'''	)

            return dummy_inputs







       @property
       def snake_case__		(				self  :						List[Any]	)       ->	float:

            '''simple docstring'''




            return 1e-3



