
"""simple docstring"""


import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						def __init__( self     :      List[str]  ,A_     :      Union[str, Any]  ,A_     :      Any=None  ,A_     :      Optional[Any]=True  ,A_     :      List[str]=None  ,**A_     :      str      )     ->   str:
							A						   =   parent
							A						   =   config_class
							A						   =   has_text_modality
							A						   =   kwargs
							A						   =   common_properties


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Dict:
							A						   =   self.config_class(**self.inputs_dict      )
							A						   =   (
							    ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
							    if self.common_properties is None
							    else self.common_properties
							)

							# Add common fields for text models
							if self.has_text_modality:
								common_properties.extend(['vocab_size']      )

							# Test that config has the common properties as getters
							for prop in common_properties:
								self.parent.assertTrue(hasattr(A_  ,A_      )  ,msg=F'`{prop}` does not exist'      )

							# Test that config has the common properties as setter
							for idx, name in enumerate(A_      ):
								try:
									setattr(A_  ,A_  ,A_      )
									self.parent.assertEqual(
									    getattr(A_  ,A_      )  ,A_  ,msg=F'`{name} value {idx} expected, but was {getattr(A_  ,A_      )}'      )
								except NotImplementedError:
									# Some models might not be able to implement setters for common_properties
									# In that case, a NotImplementedError is raised
									pass

        # Test if config class can be called with Config(prop_name=..)
							for idx, name in enumerate(A_      ):
								try:
									A						   =   self.config_class(**{name: idx}      )
									self.parent.assertEqual(
									    getattr(A_  ,A_      )  ,A_  ,msg=F'`{name} value {idx} expected, but was {getattr(A_  ,A_      )}'      )
								except NotImplementedError:
									# Some models might not be able to implement setters for common_properties
									# In that case, a NotImplementedError is raised
									pass


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Tuple:
							A						   =   self.config_class(**self.inputs_dict      )
							A						   =   json.loads(config.to_json_string()      )
							for key, value in self.inputs_dict.items():
								self.parent.assertEqual(obj[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Dict:
							A						   =   self.config_class(**self.inputs_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   os.path.join(A_  ,'config.json'      )
								config_first.to_json_file(A_      )
								A						   =   self.config_class.from_json_file(A_      )

							self.parent.assertEqual(config_second.to_dict()  ,config_first.to_dict()      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   str:
							A						   =   self.config_class(**self.inputs_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								config_first.save_pretrained(A_      )
								A						   =   self.config_class.from_pretrained(A_      )

							self.parent.assertEqual(config_second.to_dict()  ,config_first.to_dict()      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Tuple:
							A						   =   self.config_class(**self.inputs_dict      )

							A						   =   'test'
							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   os.path.join(A_  ,A_      )
								config_first.save_pretrained(A_      )
								A						   =   self.config_class.from_pretrained(A_  ,subfolder=A_      )

							self.parent.assertEqual(config_second.to_dict()  ,config_first.to_dict()      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[int]:
							A						   =   self.config_class(**self.inputs_dict  ,num_labels=5      )
							self.parent.assertEqual(len(config.idalabel      )  ,5      )
							self.parent.assertEqual(len(config.labelaid      )  ,5      )

							A						   =   3
							self.parent.assertEqual(len(config.idalabel      )  ,3      )
							self.parent.assertEqual(len(config.labelaid      )  ,3      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   List[str]:
							if self.config_class.is_composition:
								return
							A						   =   self.config_class()
							self.parent.assertIsNotNone(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Tuple:
							A						   =   copy.deepcopy(A_      )
							A						   =   self.config_class(**A_      )
							A						   =   []
							for key, value in config_common_kwargs.items():
								if key == "torch_dtype":
									if not is_torch_available():
										continue
									else:
										import torch

										if config.torch_dtype != torch.floataa:
											wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa)      )
								elif getattr(A_  ,A_      ) != value:
									wrong_values.append((key, getattr(A_  ,A_      ), value)      )

							if len(A_      ) > 0:
								A						   =   '\n'.join([F'- {v[0]}: got {v[1]} instead of {v[2]}' for v in wrong_values]      )
								raise ValueError(F'The following keys were not properly set in the config:\n{errors}'      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   int:
							self.create_and_test_config_common_properties()
							self.create_and_test_config_to_json_string()
							self.create_and_test_config_to_json_file()
							self.create_and_test_config_from_and_save_pretrained()
							self.create_and_test_config_from_and_save_pretrained_subfolder()
							self.create_and_test_config_with_num_labels()
							self.check_config_can_be_init_without_params()
							self.check_config_arguments_init()

"""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)