


"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    SwinConfig(image_size=192   )

			if "base" in model_name:
						__lowerCAmelCase       					=    6
						__lowerCAmelCase       					=    128
						__lowerCAmelCase       					=    (2, 2, 18, 2)
						__lowerCAmelCase       					=    (4, 8, 16, 32)
			elif "large" in model_name:
						__lowerCAmelCase       					=    12
						__lowerCAmelCase       					=    192
						__lowerCAmelCase       					=    (2, 2, 18, 2)
						__lowerCAmelCase       					=    (6, 12, 24, 48)
			else:
						raise ValueError("Model not supported, only supports base and large variants"   )

			__lowerCAmelCase       					=    window_size
			__lowerCAmelCase       					=    embed_dim
			__lowerCAmelCase       					=    depths
			__lowerCAmelCase       					=    num_heads

			return config


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			if "encoder.mask_token" in name:
						__lowerCAmelCase       					=    name.replace("encoder.mask_token"					,				"embeddings.mask_token"   )
			if "encoder.patch_embed.proj" in name:
						__lowerCAmelCase       					=    name.replace("encoder.patch_embed.proj"					,				"embeddings.patch_embeddings.projection"   )
			if "encoder.patch_embed.norm" in name:
						__lowerCAmelCase       					=    name.replace("encoder.patch_embed.norm"					,				"embeddings.norm"   )
			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 name == "encoder.norm.weight":
						__lowerCAmelCase       					=    "layernorm.weight"
			if name == "encoder.norm.bias":
						__lowerCAmelCase       					=    "layernorm.bias"

			if "decoder" in name:
						pass
			else:
						__lowerCAmelCase       					=    "swin." + name

			return name


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			for key in orig_state_dict.copy().keys():
						__lowerCAmelCase       					=    orig_state_dict.pop(_UpperCamelCase   )

						if "attn_mask" in key:
									pass
						elif "qkv" in key:
									__lowerCAmelCase       					=    key.split("."   )
									__lowerCAmelCase       					=    int(key_split[2]   )
									__lowerCAmelCase       					=    int(key_split[4]   )
									__lowerCAmelCase       					=    model.swin.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 _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    torch.load(_UpperCamelCase					,				map_location="cpu"   )["model"]

			__lowerCAmelCase       					=    get_swin_config(_UpperCamelCase   )
			__lowerCAmelCase       					=    SwinForMaskedImageModeling(_UpperCamelCase   )
			model.eval()

			__lowerCAmelCase       					=    convert_state_dict(_UpperCamelCase					,				_UpperCamelCase   )
			model.load_state_dict(_UpperCamelCase   )

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

			__lowerCAmelCase       					=    ViTImageProcessor(size={"height": 192, "width": 192}   )
			__lowerCAmelCase       					=    Image.open(requests.get(_UpperCamelCase					,				stream=_UpperCamelCase   ).raw   )
			__lowerCAmelCase       					=    image_processor(images=_UpperCamelCase					,				return_tensors="pt"   )

			with torch.no_grad():
						__lowerCAmelCase       					=    model(**_UpperCamelCase   ).logits

			print(outputs.keys()   )
			print("Looks ok!"   )

			if pytorch_dump_folder_path is not None:
						print(f"Saving model {model_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   )

			if push_to_hub:
						print(f"Pushing model and image processor for {model_name} to hub"   )
						model.push_to_hub(f"microsoft/{model_name}"   )
						image_processor.push_to_hub(f"microsoft/{model_name}"   )


if __name__ == "__main__":
	A  :     str    					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="swin-base-simmim-window6-192",
	    type=str,
	    choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
	    help="Name of the Swin SimMIM model you'd like to convert.",
	)
	parser.add_argument(
	    "--checkpoint_path",
	    default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
	    type=str,
	    help="Path to the original PyTorch checkpoint (.pth file).",
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
	)
	parser.add_argument(
	    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
	)

	A  :     Optional[Any]    					=    parser.parse_args()
	convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf

	from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self					,     __a			):
										for model_result in results.values():
													for batch_size, sequence_length in zip(model_result["bs"]					,     model_result["ss"]			):
																__lowerCAmelCase       					=    model_result["result"][batch_size][sequence_length]
																self.assertIsNotNone(__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     eager_mode=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sgugger/tiny-distilbert-classification"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     only_pretrain_model=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     eager_mode=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result			)
										self.check_results_dict_not_empty(results.memory_train_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result			)
										self.check_results_dict_not_empty(results.memory_train_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "patrickvonplaten/t5-tiny-random"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     configs=[config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"			)			) == 0					,     "Cannot do xla on CPU."			)
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     use_xla=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]					,     inference=__a					,     save_to_csv=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     inference_time_csv_file=os.path.join(__a					,     "inf_time.csv"			)					,     inference_memory_csv_file=os.path.join(__a					,     "inf_mem.csv"			)					,     env_info_csv_file=os.path.join(__a					,     "env.csv"			)					,     multi_process=__a					,     )
													__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
													benchmark.run()
													self.assertTrue(Path(os.path.join(__a					,     "inf_time.csv"			)			).exists()			)
													self.assertTrue(Path(os.path.join(__a					,     "inf_mem.csv"			)			).exists()			)
													self.assertTrue(Path(os.path.join(__a					,     "env.csv"			)			).exists()			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"

										def _check_summary_is_not_empty(__a			):
													self.assertTrue(hasattr(__a					,     "sequential"			)			)
													self.assertTrue(hasattr(__a					,     "cumulative"			)			)
													self.assertTrue(hasattr(__a					,     "current"			)			)
													self.assertTrue(hasattr(__a					,     "total"			)			)

										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     log_filename=os.path.join(__a					,     "log.txt"			)					,     log_print=__a					,     trace_memory_line_by_line=__a					,     eager_mode=__a					,     multi_process=__a					,     )
													__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
													__lowerCAmelCase       					=    benchmark.run()
													_check_summary_is_not_empty(result.inference_summary			)
													self.assertTrue(Path(os.path.join(__a					,     "log.txt"			)			).exists()			)




