
"""simple docstring"""


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
		import torch

if is_vision_available():
		import PIL


_lowercase							   =				logging.get_logger(__name__)






def       _snake_case				(		snake_case__   :     np.ndarray	,      snake_case__   :     Union[int, Iterable[int]]	,      snake_case__   :     bool	,      snake_case__   :     int     ):
	def constraint_to_multiple_of(snake_case__   :     Dict	,      snake_case__   :     Optional[Any]	,      snake_case__   :     Union[str, Any]=0	,      snake_case__   :     List[Any]=None     ):
		A						   =   round(val / multiple     ) * multiple

		if max_val is not None and x > max_val:
			A						   =   math.floor(val / multiple     ) * multiple

		if x < min_val:
			A						   =   math.ceil(val / multiple     ) * multiple

		return x

	A						   =   (output_size, output_size) if isinstance(snake_case__	,      snake_case__     ) else output_size

	A   ,      A						   =   get_image_size(snake_case__     )
	A   ,      A						   =   output_size

	# determine new height and width
	A						   =   output_height / input_height
	A						   =   output_width / input_width

	if keep_aspect_ratio:
		# scale as little as possible
		if abs(1 - scale_width     ) < abs(1 - scale_height     ):
			# fit width
			A						   =   scale_width
		else:
			# fit height
			A						   =   scale_height

	A						   =   constraint_to_multiple_of(scale_height * input_height	,      multiple=snake_case__     )
	A						   =   constraint_to_multiple_of(scale_width * input_width	,      multiple=snake_case__     )

	return (new_height, new_width)







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    str      =    ['''pixel_values''']


						def __init__( self     :      Optional[Any]  ,A_     :      bool = True  ,A_     :      Dict[str, int] = None  ,A_     :      PILImageResampling = PILImageResampling.BILINEAR  ,A_     :      bool = False  ,A_     :      int = 1  ,A_     :      bool = True  ,A_     :      Union[int, float] = 1 / 255  ,A_     :      bool = True  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,**A_     :      Any  ,)     ->   None:
							super().__init__(**A_      )
							A						   =   size if size is not None else {'height': 384, 'width': 384}
							A						   =   get_size_dict(A_      )
							A						   =   do_resize
							A						   =   size
							A						   =   keep_aspect_ratio
							A						   =   ensure_multiple_of
							A						   =   resample
							A						   =   do_rescale
							A						   =   rescale_factor
							A						   =   do_normalize
							A						   =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							A						   =   image_std if image_std is not None else IMAGENET_STANDARD_STD


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      np.ndarray  ,A_     :      Dict[str, int]  ,A_     :      bool = False  ,A_     :      int = 1  ,A_     :      PILImageResampling = PILImageResampling.BICUBIC  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      int  ,)     ->   np.ndarray:
							A						   =   get_size_dict(A_      )
							if "height" not in size or "width" not in size:
								raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'      )
							A						   =   get_resize_output_image_size(
							    A_  ,output_size=(size['height'], size['width'])  ,keep_aspect_ratio=A_  ,multiple=A_  ,)
							return resize(A_  ,size=A_  ,resample=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      np.ndarray  ,A_     :      Union[int, float]  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      List[str]  ,)     ->   Tuple:
							return rescale(A_  ,scale=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      np.ndarray  ,A_     :      Union[float, List[float]]  ,A_     :      Union[float, List[float]]  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      Tuple  ,)     ->   np.ndarray:
							return normalize(A_  ,mean=A_  ,std=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      ImageInput  ,A_     :      bool = None  ,A_     :      int = None  ,A_     :      bool = None  ,A_     :      int = None  ,A_     :      PILImageResampling = None  ,A_     :      bool = None  ,A_     :      float = None  ,A_     :      bool = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[str, TensorType]] = None  ,A_     :      ChannelDimension = ChannelDimension.FIRST  ,**A_     :      Optional[Any]  ,)     ->   PIL.Image.Image:
							A						   =   do_resize if do_resize is not None else self.do_resize
							A						   =   size if size is not None else self.size
							A						   =   get_size_dict(A_      )
							A						   =   keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
							A						   =   ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
							A						   =   resample if resample is not None else self.resample
							A						   =   do_rescale if do_rescale is not None else self.do_rescale
							A						   =   rescale_factor if rescale_factor is not None else self.rescale_factor
							A						   =   do_normalize if do_normalize is not None else self.do_normalize
							A						   =   image_mean if image_mean is not None else self.image_mean
							A						   =   image_std if image_std is not None else self.image_std

							A						   =   make_list_of_images(A_      )

							if not valid_images(A_      ):
								raise ValueError(
								    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
								    'torch.Tensor, tf.Tensor or jax.ndarray.'      )

							if do_resize and size is None or resample is None:
								raise ValueError('Size and resample must be specified if do_resize is True.'      )

							if do_rescale and rescale_factor is None:
								raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

							if do_normalize and (image_mean is None or image_std is None):
								raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

							# All transformations expect numpy arrays.
							A						   =   [to_numpy_array(A_      ) for image in images]

							if do_resize:
								A						   =   [self.resize(image=A_  ,size=A_  ,resample=A_      ) for image in images]

							if do_rescale:
								A						   =   [self.rescale(image=A_  ,scale=A_      ) for image in images]

							if do_normalize:
								A						   =   [self.normalize(image=A_  ,mean=A_  ,std=A_      ) for image in images]

							A						   =   [to_channel_dimension_format(A_  ,A_      ) for image in images]

							A						   =   {'pixel_values': images}
							return BatchFeature(data=A_  ,tensor_type=A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Dict  ,A_     :      List[Tuple] = None      )     ->   Tuple:
							A						   =   outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
								if len(A_      ) != len(A_      ):
									raise ValueError(
									    'Make sure that you pass in as many target sizes as the batch dimension of the logits'      )

								if is_torch_tensor(A_      ):
									A						   =   target_sizes.numpy()

								A						   =   []

								for idx in range(len(A_      )      ):
									A						   =   torch.nn.functional.interpolate(
									    logits[idx].unsqueeze(dim=0      )  ,size=target_sizes[idx]  ,mode='bilinear'  ,align_corners=A_      )
									A						   =   resized_logits[0].argmax(dim=0      )
									semantic_segmentation.append(A_      )
							else:
								A						   =   logits.argmax(dim=1      )
								A						   =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

							return semantic_segmentation

"""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       _snake_case				(		snake_case__   :     int     ):
	A						   =   SwinvaConfig()
	A						   =   swinva_name.split('_'     )

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

	if model_size == "tiny":
		A						   =   96
		A						   =   (2, 2, 6, 2)
		A						   =   (3, 6, 12, 24)
	elif model_size == "small":
		A						   =   96
		A						   =   (2, 2, 18, 2)
		A						   =   (3, 6, 12, 24)
	elif model_size == "base":
		A						   =   128
		A						   =   (2, 2, 18, 2)
		A						   =   (4, 8, 16, 32)
	else:
		A						   =   192
		A						   =   (2, 2, 18, 2)
		A						   =   (6, 12, 24, 48)

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

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		A						   =   2_1841
		A						   =   'huggingface/label-files'
		A						   =   'imagenet-22k-id2label.json'
		A						   =   json.load(open(hf_hub_download(snake_case__	,      snake_case__	,      repo_type='dataset'     )	,      'r'     )     )
		A						   =   {int(snake_case__     ): v for k, v in idalabel.items()}
		A						   =   idalabel
		A						   =   {v: k for k, v in idalabel.items()}

	else:
		A						   =   1000
		A						   =   'huggingface/label-files'
		A						   =   'imagenet-1k-id2label.json'
		A						   =   json.load(open(hf_hub_download(snake_case__	,      snake_case__	,      repo_type='dataset'     )	,      'r'     )     )
		A						   =   {int(snake_case__     ): v for k, v in idalabel.items()}
		A						   =   idalabel
		A						   =   {v: k for k, v in idalabel.items()}

	A						   =   img_size
	A						   =   num_classes
	A						   =   embed_dim
	A						   =   depths
	A						   =   num_heads
	A						   =   window_size

	return config






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

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

	return name






def       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     List[Any]     ):
	for key in orig_state_dict.copy().keys():
		A						   =   orig_state_dict.pop(snake_case__     )

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

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

	return orig_state_dict






def       _snake_case				(		snake_case__   :     Optional[int]	,      snake_case__   :     Tuple     ):
	A						   =   timm.create_model(snake_case__	,      pretrained=snake_case__     )
	timm_model.eval()

	A						   =   get_swinva_config(snake_case__     )
	A						   =   SwinvaForImageClassification(snake_case__     )
	model.eval()

	A						   =   convert_state_dict(timm_model.state_dict()	,      snake_case__     )
	model.load_state_dict(snake_case__     )

	A						   =   'http://images.cocodataset.org/val2017/000000039769.jpg'

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

	A						   =   timm_model(inputs['pixel_values']     )
	A						   =   model(**snake_case__     ).logits

	assert torch.allclose(snake_case__	,      snake_case__	,      atol=1e-3     )

	print(F'Saving model {swinva_name} to {pytorch_dump_folder_path}'     )
	model.save_pretrained(snake_case__     )

	print(F'Saving image processor to {pytorch_dump_folder_path}'     )
	image_processor.save_pretrained(snake_case__     )

	model.push_to_hub(
	    repo_path_or_name=Path(snake_case__	,      snake_case__     )	,      organization='nandwalritik'	,      commit_message='Add model'	,      )


if __name__ == "__main__":
		_lowercase							   =				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.'''
		)

		_lowercase							   =				parser.parse_args()
		convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)