



from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


snake_case__					:     Dict    		=   logging.get_logger(__name__)

class   _A   ( _lowercase	):


						'''simple docstring'''





						def __init__(	self	:							List[Any]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int							,      lowerCamelCase	:							float							,      **lowerCamelCase	:							Any	):






							'''simple docstring'''



							__lowercase		       =			feature_size
							__lowercase		       =			sampling_rate
							__lowercase		       =			padding_value

							__lowercase		       =			kwargs.pop("padding_side"							,      "right"	)
							__lowercase		       =			kwargs.pop("return_attention_mask"							,      lowerCamelCase	)

							super().__init__(**lowerCamelCase	)



						def     _snake_case	(	self	:							Any							,      lowerCamelCase	:							Union[
						        BatchFeature,
						        List[BatchFeature],
						        Dict[str, BatchFeature],
						        Dict[str, List[BatchFeature]],
						        List[Dict[str, BatchFeature]],
						    ]							,      lowerCamelCase	:							Union[bool, str, PaddingStrategy] = True							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							bool = False							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							Optional[bool] = None							,      lowerCamelCase	:							Optional[Union[str, TensorType]] = None							,      ):






							'''simple docstring'''



							if isinstance(lowerCamelCase							,      (list, tuple)	) and isinstance(processed_features[0]							,      (dict, BatchFeature)	):
								__lowercase		       =			{
								    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
								}

							# The model's main input name, usually `input_values`, has be passed for padding
							if self.model_input_names[0] not in processed_features:
								raise ValueError(
								    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
								    f""" to this method that includes {self.model_input_names[0]}, but you provided"""
								    f""" {list(processed_features.keys()	)}"""	)

							__lowercase		       =			processed_features[self.model_input_names[0]]
							__lowercase		       =			(
							    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
							)

							if len(lowerCamelCase	) == 0:
								if return_attention_mask:
									__lowercase		       =			[]
								return processed_features

							# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
							# and rebuild them afterwards if no return_tensors is specified
							# Note that we lose the specific device the tensor may be on for PyTorch

							__lowercase		       =			required_input[0]
							if isinstance(lowerCamelCase							,      (list, tuple)	):
								# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
								__lowercase		       =			0
								while len(required_input[index]	) == 0:
									index += 1
								if index < len(lowerCamelCase	):
									__lowercase		       =			required_input[index][0]

							if return_tensors is None:
								if is_tf_tensor(lowerCamelCase	):
									__lowercase		       =			"tf"
								elif is_torch_tensor(lowerCamelCase	):
									__lowercase		       =			"pt"
								elif isinstance(lowerCamelCase							,      (int, float, list, tuple, np.ndarray)	):
									__lowercase		       =			"np"
								else:
									raise ValueError(
									    f"""type of {first_element} unknown: {type(lowerCamelCase	)}. """
									    "Should be one of a python, numpy, pytorch or tensorflow object."	)

							for key, value in processed_features.items():
								if isinstance(value[0]							,      (int, float)	):
									__lowercase		       =			to_numpy(lowerCamelCase	)
								else:
									__lowercase		       =			[to_numpy(lowerCamelCase	) for v in value]

        # Convert padding_strategy in PaddingStrategy
							__lowercase		       =			self._get_padding_strategies(padding=lowerCamelCase							,      max_length=lowerCamelCase	)

							__lowercase		       =			processed_features[self.model_input_names[0]]

							__lowercase		       =			len(lowerCamelCase	)
							if not all(len(lowerCamelCase	) == batch_size for v in processed_features.values()	):
								raise ValueError("Some items in the output dictionary have a different batch size than others."	)

							__lowercase		       =			[]
							for i in range(lowerCamelCase	):
								__lowercase		       =			{k: v[i] for k, v in processed_features.items()}
								# truncation
								__lowercase		       =			self._truncate(
								    lowerCamelCase							,      max_length=lowerCamelCase							,      pad_to_multiple_of=lowerCamelCase							,      truncation=lowerCamelCase							,      )
								truncated_inputs.append(lowerCamelCase	)

							if padding_strategy == PaddingStrategy.LONGEST:
								# make sure that `max_length` cannot be longer than the longest truncated length
								__lowercase		       =			max(len(input_slice[self.model_input_names[0]]	) for input_slice in truncated_inputs	)
								__lowercase		       =			PaddingStrategy.MAX_LENGTH

							__lowercase		       =			{}
							for i in range(lowerCamelCase	):
								# padding
								__lowercase		       =			self._pad(
								    truncated_inputs[i]							,      max_length=lowerCamelCase							,      padding_strategy=lowerCamelCase							,      pad_to_multiple_of=lowerCamelCase							,      return_attention_mask=lowerCamelCase							,      )

								for key, value in outputs.items():
									if key not in batch_outputs:
										__lowercase		       =			[]
									if value.dtype is np.dtype(np.floataa	):
										__lowercase		       =			value.astype(np.floataa	)
									batch_outputs[key].append(lowerCamelCase	)

							return BatchFeature(lowerCamelCase							,      tensor_type=lowerCamelCase	)



						def     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							Union[Dict[str, np.ndarray], BatchFeature]							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							Optional[bool] = None							,      ):






							'''simple docstring'''



							__lowercase		       =			processed_features[self.model_input_names[0]]

							if padding_strategy == PaddingStrategy.LONGEST:
								__lowercase		       =			len(lowerCamelCase	)

							if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
								__lowercase		       =			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

							__lowercase		       =			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCamelCase	) < max_length

							if return_attention_mask and "attention_mask" not in processed_features:
								__lowercase		       =			np.ones(len(lowerCamelCase	)							,      dtype=np.intaa	)

							if needs_to_be_padded:
								__lowercase		       =			max_length - len(lowerCamelCase	)
								if self.padding_side == "right":
									if return_attention_mask:
										__lowercase		       =			np.pad(
										    processed_features["attention_mask"]							,      (0, difference)	)
									__lowercase		       =			((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
									__lowercase		       =			np.pad(
									    lowerCamelCase							,      lowerCamelCase							,      "constant"							,      constant_values=self.padding_value	)
								elif self.padding_side == "left":
									if return_attention_mask:
										__lowercase		       =			np.pad(
										    processed_features["attention_mask"]							,      (difference, 0)	)
									__lowercase		       =			((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
									__lowercase		       =			np.pad(
									    lowerCamelCase							,      lowerCamelCase							,      "constant"							,      constant_values=self.padding_value	)
								else:
									raise ValueError("Invalid padding strategy:" + str(self.padding_side	)	)

							return processed_features



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							Union[Dict[str, np.ndarray], BatchFeature]							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							Optional[bool] = None							,      ):






							'''simple docstring'''



							if not truncation:
								return processed_features
							elif truncation and max_length is None:
								raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."	)

							__lowercase		       =			processed_features[self.model_input_names[0]]

							# find `max_length` that fits `pad_to_multiple_of`
							if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
								__lowercase		       =			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

							__lowercase		       =			len(lowerCamelCase	) > max_length

							if needs_to_be_truncated:
								__lowercase		       =			processed_features[self.model_input_names[0]][:max_length]
								if "attention_mask" in processed_features:
									__lowercase		       =			processed_features["attention_mask"][:max_length]

							return processed_features



						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							Tuple=False							,      lowerCamelCase	:							str=None	):






							'''simple docstring'''



							if padding is not False:
								if padding is True:
									__lowercase		       =			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
								elif not isinstance(lowerCamelCase							,      lowerCamelCase	):
									__lowercase		       =			PaddingStrategy(lowerCamelCase	)
								elif isinstance(lowerCamelCase							,      lowerCamelCase	):
									__lowercase		       =			padding
							else:
								__lowercase		       =			PaddingStrategy.DO_NOT_PAD

							# Set max length if needed
							if max_length is None:
								if padding_strategy == PaddingStrategy.MAX_LENGTH:
									raise ValueError(
									    f"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""	)

        # Test if we have a padding value
							if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
								raise ValueError(
								    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
								    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."	)

							return padding_strategy








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    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			SwinvaConfig()
	__lowercase		       =			swinva_name.split("_"   )

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

	if model_size == "tiny":
		__lowercase		       =			9_6
		__lowercase		       =			(2, 2, 6, 2)
		__lowercase		       =			(3, 6, 1_2, 2_4)
	elif model_size == "small":
		__lowercase		       =			9_6
		__lowercase		       =			(2, 2, 1_8, 2)
		__lowercase		       =			(3, 6, 1_2, 2_4)
	elif model_size == "base":
		__lowercase		       =			1_2_8
		__lowercase		       =			(2, 2, 1_8, 2)
		__lowercase		       =			(4, 8, 1_6, 3_2)
	else:
		__lowercase		       =			1_9_2
		__lowercase		       =			(2, 2, 1_8, 2)
		__lowercase		       =			(6, 1_2, 2_4, 4_8)

	if "to" in swinva_name:
		__lowercase		       =			(1_2, 1_2, 1_2, 6)

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		__lowercase		       =			2_1_8_4_1
		__lowercase		       =			"huggingface/label-files"
		__lowercase		       =			"imagenet-22k-id2label.json"
		__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
		__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
		__lowercase		       =			idalabel
		__lowercase		       =			{v: k for k, v in idalabel.items()}

	else:
		__lowercase		       =			1_0_0_0
		__lowercase		       =			"huggingface/label-files"
		__lowercase		       =			"imagenet-1k-id2label.json"
		__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
		__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
		__lowercase		       =			idalabel
		__lowercase		       =			{v: k for k, v in idalabel.items()}

	__lowercase		       =			img_size
	__lowercase		       =			num_classes
	__lowercase		       =			embed_dim
	__lowercase		       =			depths
	__lowercase		       =			num_heads
	__lowercase		       =			window_size

	return config



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

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

	return name



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	for key in orig_state_dict.copy().keys():
		__lowercase		       =			orig_state_dict.pop(_SCREAMING_SNAKE_CASE   )

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

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

	return orig_state_dict



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			timm.create_model(_SCREAMING_SNAKE_CASE  ,	pretrained=_SCREAMING_SNAKE_CASE   )
	timm_model.eval()

	__lowercase		       =			get_swinva_config(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			SwinvaForImageClassification(_SCREAMING_SNAKE_CASE   )
	model.eval()

	__lowercase		       =			convert_state_dict(timm_model.state_dict()  ,	_SCREAMING_SNAKE_CASE   )
	model.load_state_dict(_SCREAMING_SNAKE_CASE   )

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

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

	__lowercase		       =			timm_model(inputs["pixel_values"]   )
	__lowercase		       =			model(**_SCREAMING_SNAKE_CASE   ).logits

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

	print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""   )
	model.save_pretrained(_SCREAMING_SNAKE_CASE   )

	print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
	image_processor.save_pretrained(_SCREAMING_SNAKE_CASE   )

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


if __name__ == "__main__":
	snake_case__					:     Union[str, Any]    		=   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."""
	)

	snake_case__					:     str    		=   parser.parse_args()
	convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)



