



"""simple docstring"""





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Tuple     ):


						'''simple docstring'''






						snake_case_ :			List[Any]            =      SwinConfig()
						snake_case_ :			Dict            =      swin_name.split("""_"""     )

						snake_case_ :			Union[str, Any]            =      name_split[1]
						snake_case_ :			Optional[Any]            =      int(name_split[4]     )
						snake_case_ :			Tuple            =      int(name_split[3][-1]     )

						if model_size == "tiny":
												snake_case_ :			Optional[int]            =      9_6
												snake_case_ :			Any            =      (2, 2, 6, 2)
												snake_case_ :			Optional[int]            =      (3, 6, 1_2, 2_4)
						elif model_size == "small":
												snake_case_ :			Optional[Any]            =      9_6
												snake_case_ :			List[str]            =      (2, 2, 1_8, 2)
												snake_case_ :			Tuple            =      (3, 6, 1_2, 2_4)
						elif model_size == "base":
												snake_case_ :			List[str]            =      1_2_8
												snake_case_ :			Optional[Any]            =      (2, 2, 1_8, 2)
												snake_case_ :			Optional[Any]            =      (4, 8, 1_6, 3_2)
						else:
												snake_case_ :			Dict            =      1_9_2
												snake_case_ :			Union[str, Any]            =      (2, 2, 1_8, 2)
												snake_case_ :			Tuple            =      (6, 1_2, 2_4, 4_8)

						if "in22k" in swin_name:
												snake_case_ :			int            =      2_1_8_4_1
						else:
												snake_case_ :			Dict            =      1_0_0_0
												snake_case_ :			List[Any]            =      "huggingface/label-files"
												snake_case_ :			str            =      "imagenet-1k-id2label.json"
												snake_case_ :			Any            =      json.load(open(hf_hub_download(__A       ,			__A       ,			repo_type="""dataset"""     )       ,			"""r"""     )     )
												snake_case_ :			str            =      {int(__A     ): v for k, v in idalabel.items()}
												snake_case_ :			Dict            =      idalabel
												snake_case_ :			Optional[int]            =      {v: k for k, v in idalabel.items()}

						snake_case_ :			str            =      img_size
						snake_case_ :			Union[str, Any]            =      num_classes
						snake_case_ :			str            =      embed_dim
						snake_case_ :			List[str]            =      depths
						snake_case_ :			List[str]            =      num_heads
						snake_case_ :			Tuple            =      window_size

						return config
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Union[str, Any]     ):


						'''simple docstring'''






						if "patch_embed.proj" in name:
												snake_case_ :			List[str]            =      name.replace("""patch_embed.proj"""       ,			"""embeddings.patch_embeddings.projection"""     )
						if "patch_embed.norm" in name:
												snake_case_ :			Any            =      name.replace("""patch_embed.norm"""       ,			"""embeddings.norm"""     )
						if "layers" in name:
												snake_case_ :			Optional[Any]            =      "encoder." + name
						if "attn.proj" in name:
												snake_case_ :			Dict            =      name.replace("""attn.proj"""       ,			"""attention.output.dense"""     )
						if "attn" in name:
												snake_case_ :			Any            =      name.replace("""attn"""       ,			"""attention.self"""     )
						if "norm1" in name:
												snake_case_ :			int            =      name.replace("""norm1"""       ,			"""layernorm_before"""     )
						if "norm2" in name:
												snake_case_ :			List[str]            =      name.replace("""norm2"""       ,			"""layernorm_after"""     )
						if "mlp.fc1" in name:
												snake_case_ :			Dict            =      name.replace("""mlp.fc1"""       ,			"""intermediate.dense"""     )
						if "mlp.fc2" in name:
												snake_case_ :			Dict            =      name.replace("""mlp.fc2"""       ,			"""output.dense"""     )

						if name == "norm.weight":
												snake_case_ :			int            =      "layernorm.weight"
						if name == "norm.bias":
												snake_case_ :			Union[str, Any]            =      "layernorm.bias"

						if "head" in name:
												snake_case_ :			Optional[int]            =      name.replace("""head"""       ,			"""classifier"""     )
						else:
												snake_case_ :			List[str]            =      "swin." + name

						return name
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Any       ,			__UpperCamelCase   :      Any     ):


						'''simple docstring'''






						for key in orig_state_dict.copy().keys():
												snake_case_ :			Optional[Any]            =      orig_state_dict.pop(__A     )

												if "mask" in key:
																		continue
												elif "qkv" in key:
																		snake_case_ :			Tuple            =      key.split("""."""     )
																		snake_case_ :			List[str]            =      int(key_split[1]     )
																		snake_case_ :			Any            =      int(key_split[3]     )
																		snake_case_ :			Any            =      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

						return orig_state_dict
def 						__lowerCAmelCase    (		__UpperCamelCase   :      List[Any]       ,			__UpperCamelCase   :      Any     ):


						'''simple docstring'''






						snake_case_ :			List[Any]            =      timm.create_model(__A       ,			pretrained=__A     )
						timm_model.eval()

						snake_case_ :			Optional[Any]            =      get_swin_config(__A     )
						snake_case_ :			Optional[int]            =      SwinForImageClassification(__A     )
						model.eval()

						snake_case_ :			Dict            =      convert_state_dict(timm_model.state_dict()       ,			__A     )
						model.load_state_dict(__A     )

						snake_case_ :			Optional[Any]            =      "http://images.cocodataset.org/val2017/000000039769.jpg"

						snake_case_ :			Optional[Any]            =      AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""       ,			"""-"""     )     )     )
						snake_case_ :			str            =      Image.open(requests.get(__A       ,			stream=__A     ).raw     )
						snake_case_ :			Optional[int]            =      image_processor(images=__A       ,			return_tensors="""pt"""     )

						snake_case_ :			Union[str, Any]            =      timm_model(inputs["""pixel_values"""]     )
						snake_case_ :			Union[str, Any]            =      model(**__A     ).logits

						assert torch.allclose(__A       ,			__A       ,			atol=1E-3     )

						print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'     )
						model.save_pretrained(__A     )

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


if __name__ == "__main__":
					__lowerCAmelCase							:       Optional[Any]          =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--swin_name''',
					    default='''swin_tiny_patch4_window7_224''',
					    type=str,
					    help='''Name of the Swin 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.'''
					)

					__lowerCAmelCase							:       List[str]          =       parser.parse_args()
					convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)









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 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						def __init__(self:					str ,					*__UpperCAmelCase:					Optional[int] ,					__UpperCAmelCase:					Optional[int]=None ,					__UpperCAmelCase:					Dict=None ,					**__UpperCAmelCase:					Tuple   )    ->     Dict:




											'''simple docstring'''



											super().__init__(*__UpperCAmelCase ,					**__UpperCAmelCase   )
											__a					:						str      =					eval_examples
											__a					:						List[Any]      =					post_process_function



						def        UpperCAmelCase__					(self:					Optional[int] ,					__UpperCAmelCase:					Optional[Dataset] = None ,					__UpperCAmelCase:					Optional[int]=None ,					__UpperCAmelCase:					Optional[List[str]] = None ,					__UpperCAmelCase:					str = "eval" ,					**__UpperCAmelCase:					Any ,					)    ->     Dict[str, float]:




											'''simple docstring'''



											__a					:						Optional[int]      =					gen_kwargs.copy()
											__a					:						Union[str, Any]      =					(
											    gen_kwargs["max_length"] if gen_kwargs.get("max_length"   ) is not None else self.args.generation_max_length
											)
											__a					:						Dict      =					(
											    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"   ) is not None else self.args.generation_num_beams
											)
											__a					:						Optional[Any]      =					gen_kwargs

											__a					:						Union[str, Any]      =					self.eval_dataset if eval_dataset is None else eval_dataset
											__a					:						Optional[int]      =					self.get_eval_dataloader(__UpperCAmelCase   )
											__a					:						Tuple      =					self.eval_examples if eval_examples is None else eval_examples

											# Temporarily disable metric computation, we will do it in the loop here.
											__a					:						int      =					self.compute_metrics
											__a					:						Dict      =					None
											__a					:						List[Any]      =					time.time()
											__a					:						str      =					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																__a					:						str      =					eval_loop(
																    __UpperCAmelCase ,					description="Evaluation" ,					prediction_loss_only=True if compute_metrics is None else None ,					ignore_keys=__UpperCAmelCase ,					metric_key_prefix=__UpperCAmelCase ,					)
											finally:
																__a					:						Union[str, Any]      =					compute_metrics
											__a					:						str      =					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(
											        __UpperCAmelCase ,					__UpperCAmelCase ,					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
																__a					:						Union[str, Any]      =					self.post_process_function(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )
																__a					:						Dict      =					self.compute_metrics(__UpperCAmelCase   )

																# Prefix all keys with metric_key_prefix + '_'
																for key in list(metrics.keys()   ):
																					if not key.startswith(f'{metric_key_prefix}_'   ):
																										__a					:						int      =					metrics.pop(__UpperCAmelCase   )

																metrics.update(output.metrics   )
											else:
																__a					:						Optional[int]      =					output.metrics

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

											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()   )

											__a					:						Tuple      =					self.callback_handler.on_evaluate(self.args ,					self.state ,					self.control ,					__UpperCAmelCase   )
											return metrics



						def        UpperCAmelCase__					(self:					Dict ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					Union[str, Any]=None ,					__UpperCAmelCase:					str = "test" ,					**__UpperCAmelCase:					List[Any]   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						Any      =					gen_kwargs.copy()

											__a					:						Tuple      =					self.get_test_dataloader(__UpperCAmelCase   )

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

											__a					:						Optional[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(
											        __UpperCAmelCase ,					__UpperCAmelCase ,					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

											__a					:						Optional[int]      =					self.post_process_function(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					"predict"   )
											__a					:						Dict      =					self.compute_metrics(__UpperCAmelCase   )

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()   ):
																if not key.startswith(f'{metric_key_prefix}_'   ):
																					__a					:						List[str]      =					metrics.pop(__UpperCAmelCase   )
											metrics.update(output.metrics   )
											return PredictionOutput(predictions=predictions.predictions ,					label_ids=predictions.label_ids ,					metrics=__UpperCAmelCase   )





