
'''simple docstring'''







import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm
							import torch_xla.debug.metrics as met






class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							*__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											super().__init__(*__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       eval_examples
											lowercase_	:     Tuple													=       post_process_function






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = "eval"				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       gen_kwargs.copy()
											lowercase_	:     List[str]													=       (
											    gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''		) is not None else self.args.generation_max_length
											)
											lowercase_	:     str													=       (
											    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''		) is not None else self.args.generation_num_beams
											)
											lowercase_	:     Dict													=       gen_kwargs

											lowercase_	:     List[Any]													=       self.eval_dataset if eval_dataset is None else eval_dataset
											lowercase_	:     List[str]													=       self.get_eval_dataloader(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       self.eval_examples if eval_examples is None else eval_examples

											# Temporarily disable metric computation, we will do it in the loop here.
											lowercase_	:     Union[str, Any]													=       self.compute_metrics
											lowercase_	:     Optional[int]													=       None
											lowercase_	:     Tuple													=       time.time()
											lowercase_	:     Tuple													=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																	lowercase_	:     str													=       eval_loop(
																	    __SCREAMING_SNAKE_CASE				,							description='''Evaluation'''				,							prediction_loss_only=True if compute_metrics is None else None				,							ignore_keys=__SCREAMING_SNAKE_CASE				,							metric_key_prefix=__SCREAMING_SNAKE_CASE				,							)
											finally:
																	lowercase_	:     Any													=       compute_metrics
											lowercase_	:     Any													=       self.args.eval_batch_size * self.args.world_size
											if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
																	start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
											output.metrics.update(
											    speed_metrics(
											        __SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							num_samples=output.num_samples				,							num_steps=math.ceil(output.num_samples / total_batch_size		)				,							)		)

											if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
																	# Only the main node write the results by default
																	lowercase_	:     Optional[Any]													=       self.post_process_function(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     Optional[int]													=       self.compute_metrics(__SCREAMING_SNAKE_CASE		)

																	# Prefix all keys with metric_key_prefix + '_'
																	for key in list(metrics.keys()		):
																							if not key.startswith(F'''{metric_key_prefix}_'''		):
																													lowercase_	:     List[Any]													=       metrics.pop(__SCREAMING_SNAKE_CASE		)

																	metrics.update(output.metrics		)
											else:
																	lowercase_	:     List[Any]													=       output.metrics

											if self.args.should_log:
																	# Only the main node log the results by default
																	self.log(__SCREAMING_SNAKE_CASE		)

											if self.args.tpu_metrics_debug or self.args.debug:
																	# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
																	xm.master_print(met.metrics_report()		)

											lowercase_	:     List[Any]													=       self.callback_handler.on_evaluate(self.args				,							self.state				,							self.control				,							__SCREAMING_SNAKE_CASE		)
											return metrics






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE = "test"				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       gen_kwargs.copy()

											lowercase_	:     Tuple													=       self.get_test_dataloader(__SCREAMING_SNAKE_CASE		)

											# Temporarily disable metric computation, we will do it in the loop here.
											lowercase_	:     Optional[Any]													=       self.compute_metrics
											lowercase_	:     Optional[int]													=       None
											lowercase_	:     List[Any]													=       time.time()
											lowercase_	:     int													=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																	lowercase_	:     Tuple													=       eval_loop(
																	    __SCREAMING_SNAKE_CASE				,							description='''Prediction'''				,							prediction_loss_only=True if compute_metrics is None else None				,							ignore_keys=__SCREAMING_SNAKE_CASE				,							metric_key_prefix=__SCREAMING_SNAKE_CASE				,							)
											finally:
																	lowercase_	:     Any													=       compute_metrics

											lowercase_	:     Tuple													=       self.args.eval_batch_size * self.args.world_size
											if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
																	start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
											output.metrics.update(
											    speed_metrics(
											        __SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							num_samples=output.num_samples				,							num_steps=math.ceil(output.num_samples / total_batch_size		)				,							)		)
											if self.post_process_function is None or self.compute_metrics is None:
																	return output

											lowercase_	:     Any													=       self.post_process_function(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							'''predict'''		)
											lowercase_	:     str													=       self.compute_metrics(__SCREAMING_SNAKE_CASE		)

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()		):
																	if not key.startswith(F'''{metric_key_prefix}_'''		):
																							lowercase_	:     Optional[int]													=       metrics.pop(__SCREAMING_SNAKE_CASE		)
											metrics.update(output.metrics		)
											return PredictionOutput(predictions=predictions.predictions				,							label_ids=predictions.label_ids				,							metrics=__SCREAMING_SNAKE_CASE		)








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 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     List[str]									= SwinvaConfig()
		lowerCAmelCase_			:     List[str]									= swinva_name.split('''_''' )

		lowerCAmelCase_			:     str									= name_split[1]
		if "to" in name_split[3]:
				lowerCAmelCase_			:     List[Any]									= int(name_split[3][-3:] )
		else:
				lowerCAmelCase_			:     List[Any]									= int(name_split[3] )
		if "to" in name_split[2]:
				lowerCAmelCase_			:     List[str]									= int(name_split[2][-2:] )
		else:
				lowerCAmelCase_			:     int									= int(name_split[2][6:] )

		if model_size == "tiny":
				lowerCAmelCase_			:     Any									= 96
				lowerCAmelCase_			:     List[str]									= (2, 2, 6, 2)
				lowerCAmelCase_			:     Union[str, Any]									= (3, 6, 12, 24)
		elif model_size == "small":
				lowerCAmelCase_			:     List[str]									= 96
				lowerCAmelCase_			:     Any									= (2, 2, 18, 2)
				lowerCAmelCase_			:     Dict									= (3, 6, 12, 24)
		elif model_size == "base":
				lowerCAmelCase_			:     Union[str, Any]									= 128
				lowerCAmelCase_			:     List[Any]									= (2, 2, 18, 2)
				lowerCAmelCase_			:     Tuple									= (4, 8, 16, 32)
		else:
				lowerCAmelCase_			:     Optional[Any]									= 192
				lowerCAmelCase_			:     List[Any]									= (2, 2, 18, 2)
				lowerCAmelCase_			:     List[Any]									= (6, 12, 24, 48)

		if "to" in swinva_name:
				lowerCAmelCase_			:     Union[str, Any]									= (12, 12, 12, 6)

		if ("22k" in swinva_name) and ("to" not in swinva_name):
				lowerCAmelCase_			:     Optional[int]									= 21841
				lowerCAmelCase_			:     Any									= '''huggingface/label-files'''
				lowerCAmelCase_			:     Tuple									= '''imagenet-22k-id2label.json'''
				lowerCAmelCase_			:     Any									= json.load(open(hf_hub_download(__UpperCamelCase     ,__UpperCamelCase     ,repo_type='''dataset''' )     ,'''r''' ) )
				lowerCAmelCase_			:     Optional[Any]									= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
				lowerCAmelCase_			:     str									= idalabel
				lowerCAmelCase_			:     List[str]									= {v: k for k, v in idalabel.items()}

		else:
				lowerCAmelCase_			:     Optional[int]									= 1000
				lowerCAmelCase_			:     Tuple									= '''huggingface/label-files'''
				lowerCAmelCase_			:     Union[str, Any]									= '''imagenet-1k-id2label.json'''
				lowerCAmelCase_			:     Dict									= json.load(open(hf_hub_download(__UpperCamelCase     ,__UpperCamelCase     ,repo_type='''dataset''' )     ,'''r''' ) )
				lowerCAmelCase_			:     int									= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
				lowerCAmelCase_			:     List[str]									= idalabel
				lowerCAmelCase_			:     Optional[int]									= {v: k for k, v in idalabel.items()}

		lowerCAmelCase_			:     Optional[int]									= img_size
		lowerCAmelCase_			:     Dict									= num_classes
		lowerCAmelCase_			:     Dict									= embed_dim
		lowerCAmelCase_			:     Optional[Any]									= depths
		lowerCAmelCase_			:     Optional[int]									= num_heads
		lowerCAmelCase_			:     Dict									= window_size

		return config


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

		if "head" in name:
				lowerCAmelCase_			:     int									= name.replace('''head'''     ,'''classifier''' )
		else:
				lowerCAmelCase_			:     Union[str, Any]									= '''swinv2.''' + name

		return name


def 			UpperCamelCase(  __UpperCamelCase							:		Any     ,__UpperCamelCase							:		Optional[int] ):
		for key in orig_state_dict.copy().keys():
				lowerCAmelCase_			:     Optional[int]									= orig_state_dict.pop(__UpperCamelCase )

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

						if "weight" in key:
								lowerCAmelCase_			:     Optional[Any]									= val[:dim, :]
								lowerCAmelCase_			:     Any									= val[dim : dim * 2, :]
								lowerCAmelCase_			:     List[Any]									= val[-dim:, :]
						else:
								lowerCAmelCase_			:     Dict									= val[:dim]
								lowerCAmelCase_			:     Union[str, Any]									= val[
								    dim : dim * 2
								]
								lowerCAmelCase_			:     Dict									= val[-dim:]
				else:
						lowerCAmelCase_			:     Optional[Any]									= val

		return orig_state_dict


def 			UpperCamelCase(  __UpperCamelCase							:		int     ,__UpperCamelCase							:		Dict ):
		lowerCAmelCase_			:     Optional[Any]									= timm.create_model(__UpperCamelCase     ,pretrained=__UpperCamelCase )
		timm_model.eval()

		lowerCAmelCase_			:     List[str]									= get_swinva_config(__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= SwinvaForImageClassification(__UpperCamelCase )
		model.eval()

		lowerCAmelCase_			:     str									= convert_state_dict(timm_model.state_dict()     ,__UpperCamelCase )
		model.load_state_dict(__UpperCamelCase )

		lowerCAmelCase_			:     List[Any]									= '''http://images.cocodataset.org/val2017/000000039769.jpg'''

		lowerCAmelCase_			:     Tuple									= AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''     ,'''-''' ) ) )
		lowerCAmelCase_			:     Union[str, Any]									= Image.open(requests.get(__UpperCamelCase     ,stream=__UpperCamelCase ).raw )
		lowerCAmelCase_			:     Optional[Any]									= image_processor(images=__UpperCamelCase     ,return_tensors='''pt''' )

		lowerCAmelCase_			:     List[str]									= timm_model(inputs['''pixel_values'''] )
		lowerCAmelCase_			:     Union[str, Any]									= model(**__UpperCamelCase ).logits

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

		print(f"""Saving model {swinva_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 )

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


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

						A__  :  Optional[Any]								=					parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)





