



import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class   _A   ( unittest.TestCase	):


						'''simple docstring'''





						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			0



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"	)
							self.assertIsInstance(lowerCamelCase							,      lowerCamelCase	)



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							with tempfile.TemporaryDirectory() as tmpdirname:
								__lowercase		       =			Path(lowerCamelCase	) / "preprocessor_config.json"
								__lowercase		       =			Path(lowerCamelCase	) / "config.json"
								json.dump(
								    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}							,      open(lowerCamelCase							,      "w"	)							,      )
								json.dump({"model_type": "clip"}							,      open(lowerCamelCase							,      "w"	)	)

								__lowercase		       =			AutoImageProcessor.from_pretrained(lowerCamelCase	)
								self.assertIsInstance(lowerCamelCase							,      lowerCamelCase	)



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							with tempfile.TemporaryDirectory() as tmpdirname:
								__lowercase		       =			Path(lowerCamelCase	) / "preprocessor_config.json"
								__lowercase		       =			Path(lowerCamelCase	) / "config.json"
								json.dump(
								    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}							,      open(lowerCamelCase							,      "w"	)							,      )
								json.dump({"model_type": "clip"}							,      open(lowerCamelCase							,      "w"	)	)

								__lowercase		       =			AutoImageProcessor.from_pretrained(lowerCamelCase	)
								self.assertIsInstance(lowerCamelCase							,      lowerCamelCase	)



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							with tempfile.TemporaryDirectory() as tmpdirname:
								__lowercase		       =			CLIPConfig()

								# Create a dummy config file with image_proceesor_type
								__lowercase		       =			Path(lowerCamelCase	) / "preprocessor_config.json"
								__lowercase		       =			Path(lowerCamelCase	) / "config.json"
								json.dump(
								    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}							,      open(lowerCamelCase							,      "w"	)							,      )
								json.dump({"model_type": "clip"}							,      open(lowerCamelCase							,      "w"	)	)

								# remove image_processor_type to make sure config.json alone is enough to load image processor locally
								__lowercase		       =			AutoImageProcessor.from_pretrained(lowerCamelCase	).to_dict()

								config_dict.pop("image_processor_type"	)
								__lowercase		       =			CLIPImageProcessor(**lowerCamelCase	)

								# save in new folder
								model_config.save_pretrained(lowerCamelCase	)
								config.save_pretrained(lowerCamelCase	)

								__lowercase		       =			AutoImageProcessor.from_pretrained(lowerCamelCase	)

								# make sure private variable is not incorrectly saved
								__lowercase		       =			json.loads(config.to_json_string()	)
								self.assertTrue("_processor_class" not in dict_as_saved	)

							self.assertIsInstance(lowerCamelCase							,      lowerCamelCase	)



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							with tempfile.TemporaryDirectory() as tmpdirname:
								__lowercase		       =			Path(lowerCamelCase	) / "preprocessor_config.json"
								json.dump(
								    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}							,      open(lowerCamelCase							,      "w"	)							,      )

								__lowercase		       =			AutoImageProcessor.from_pretrained(lowerCamelCase	)
								self.assertIsInstance(lowerCamelCase							,      lowerCamelCase	)



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							with self.assertRaisesRegex(
							    lowerCamelCase							,      "clip-base is not a local folder and is not a valid model identifier"	):
								__lowercase		       =			AutoImageProcessor.from_pretrained("clip-base"	)



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							with self.assertRaisesRegex(
							    lowerCamelCase							,      R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"	):
								__lowercase		       =			AutoImageProcessor.from_pretrained(lowerCamelCase							,      revision="aaaaaa"	)



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							with self.assertRaisesRegex(
							    lowerCamelCase							,      "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."							,      ):
								__lowercase		       =			AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model"	)



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							with self.assertRaises(lowerCamelCase	):
								__lowercase		       =			AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor"	)
							# If remote code is disabled, we can't load this config.
							with self.assertRaises(lowerCamelCase	):
								__lowercase		       =			AutoImageProcessor.from_pretrained(
								    "hf-internal-testing/test_dynamic_image_processor"							,      trust_remote_code=lowerCamelCase	)

							__lowercase		       =			AutoImageProcessor.from_pretrained(
							    "hf-internal-testing/test_dynamic_image_processor"							,      trust_remote_code=lowerCamelCase	)
							self.assertEqual(image_processor.__class__.__name__							,      "NewImageProcessor"	)

							# Test image processor can be reloaded.
							with tempfile.TemporaryDirectory() as tmp_dir:
								image_processor.save_pretrained(lowerCamelCase	)
								__lowercase		       =			AutoImageProcessor.from_pretrained(lowerCamelCase							,      trust_remote_code=lowerCamelCase	)
							self.assertEqual(reloaded_image_processor.__class__.__name__							,      "NewImageProcessor"	)



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							try:
								AutoConfig.register("custom"							,      lowerCamelCase	)
								AutoImageProcessor.register(lowerCamelCase							,      lowerCamelCase	)
								# Trying to register something existing in the Transformers library will raise an error
								with self.assertRaises(lowerCamelCase	):
									AutoImageProcessor.register(lowerCamelCase							,      lowerCamelCase	)

								with tempfile.TemporaryDirectory() as tmpdirname:
									__lowercase		       =			Path(lowerCamelCase	) / "preprocessor_config.json"
									__lowercase		       =			Path(lowerCamelCase	) / "config.json"
									json.dump(
									    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}							,      open(lowerCamelCase							,      "w"	)							,      )
									json.dump({"model_type": "clip"}							,      open(lowerCamelCase							,      "w"	)	)

									__lowercase		       =			CustomImageProcessor.from_pretrained(lowerCamelCase	)

								# Now that the config is registered, it can be used as any other config with the auto-API
								with tempfile.TemporaryDirectory() as tmp_dir:
									image_processor.save_pretrained(lowerCamelCase	)
									__lowercase		       =			AutoImageProcessor.from_pretrained(lowerCamelCase	)
									self.assertIsInstance(lowerCamelCase							,      lowerCamelCase	)

							finally:
								if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
								if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
									del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							class   _A   ( _lowercase	):


													'''simple docstring'''





													_snake_case			:     Union[str, Any]      							=      True

							try:
								AutoConfig.register("custom"							,      lowerCamelCase	)
								AutoImageProcessor.register(lowerCamelCase							,      lowerCamelCase	)
								# If remote code is not set, the default is to use local
								__lowercase		       =			AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor"	)
								self.assertEqual(image_processor.__class__.__name__							,      "NewImageProcessor"	)
								self.assertTrue(image_processor.is_local	)

								# If remote code is disabled, we load the local one.
								__lowercase		       =			AutoImageProcessor.from_pretrained(
								    "hf-internal-testing/test_dynamic_image_processor"							,      trust_remote_code=lowerCamelCase	)
								self.assertEqual(image_processor.__class__.__name__							,      "NewImageProcessor"	)
								self.assertTrue(image_processor.is_local	)

								# If remote is enabled, we load from the Hub
								__lowercase		       =			AutoImageProcessor.from_pretrained(
								    "hf-internal-testing/test_dynamic_image_processor"							,      trust_remote_code=lowerCamelCase	)
								self.assertEqual(image_processor.__class__.__name__							,      "NewImageProcessor"	)
								self.assertTrue(not hasattr(lowerCamelCase							,      "is_local"	)	)

							finally:
								if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
								if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
									del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]








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)



