


import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length






def 							__lowercase       (			__lowerCAmelCase							:    Tuple       ):
							a__					=					int(__lowerCAmelCase       )
							a__				, a__				, a__					=					t // 3_6_0_0, (t // 6_0) % 6_0, t % 6_0
							return F'{h}:{m:02d}:{s:02d}' if h != 0 else F'{m:02d}:{s:02d}'






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    Optional[Any]=3_0_0       ):
							# docstyle-ignore
							return F'\n    <div>\n      {prefix}\n      <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>\n      {label}\n    </div>\n    '






def 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any]       ):
							a__					=					'<table border="1" class="dataframe">\n'
							html_code += """  <thead>\n <tr style="text-align: left;">\n"""
							for i in items[0]:
														html_code += F'      <th>{i}</th>\n'
							html_code += "    </tr>\n  </thead>\n  <tbody>\n"
							for line in items[1:]:
														html_code += "    <tr>\n"
														for elt in line:
																					a__					=					F'{elt:.6f}' if isinstance(__lowerCAmelCase ,   __lowerCAmelCase       ) else str(__lowerCAmelCase       )
																					html_code += F'      <td>{elt}</td>\n'
														html_code += "    </tr>\n"
							html_code += "  </tbody>\n</table><p>"
							return html_code







class 	snake_case_       :
					UpperCAmelCase__ :   Optional[int]   				=      5
					UpperCAmelCase__ :   str   				=      0.2
					def __init__(				self       :Any      ,__snake_case       :int      ,__snake_case       :Optional[str] = None      ,__snake_case       :bool = True      ,__snake_case       :Optional["NotebookTrainingTracker"] = None      ,__snake_case       :int = 3_00      ,) ->   Dict:
												a__					=					total
												a__					=					'' if prefix is None else prefix
												a__					=					leave
												a__					=					parent
												a__					=					width
												a__					=					None
												a__					=					None
												a__					=					None
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :int      ,__snake_case       :bool = False      ,__snake_case       :str = None				) ->   int:
												a__					=					value
												if comment is not None:
																			a__					=					comment
												if self.last_value is None:
																			a__					=					a__					=					time.time()
																			a__					=					a__					=					value
																			a__					=					a__					=					None
																			a__					=					self.warmup
																			a__					=					1
																			self.update_bar(__snake_case				)
												elif value <= self.last_value and not force_update:
																			return
												elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for      ,self.total				):
																			if self.first_calls > 0:
																										self.first_calls -= 1
																			a__					=					time.time()
																			a__					=					current_time - self.start_time
																			# We could have value = self.start_value if the update is called twixe with the same start value.
																			if value > self.start_value:
																										a__					=					self.elapsed_time / (value - self.start_value)
																			else:
																										a__					=					None
																			if value >= self.total:
																										a__					=					self.total
																										a__					=					None
																										if not self.leave:
																																	self.close()
																			elif self.average_time_per_item is not None:
																										a__					=					self.average_time_per_item * (self.total - value)
																			self.update_bar(__snake_case				)
																			a__					=					value
																			a__					=					current_time
																			if self.average_time_per_item is None:
																										a__					=					1
																			else:
																										a__					=					max(int(self.update_every / self.average_time_per_item				)      ,1				)
					def 			lowerCamelCase__(				self       :int      ,__snake_case       :Optional[int]      ,__snake_case       :Optional[Any]=None				) ->   Optional[Any]:
												a__					=					' ' * (len(str(self.total				)				) - len(str(__snake_case				)				)) + str(__snake_case				)
												if self.elapsed_time is None:
																			a__					=					F'[{spaced_value}/{self.total} : < :'
												elif self.predicted_remaining is None:
																			a__					=					F'[{spaced_value}/{self.total} {format_time(self.elapsed_time				)}'
												else:
																			a__					=					(
																			    F'[{spaced_value}/{self.total} {format_time(self.elapsed_time				)} <'
																			    F' {format_time(self.predicted_remaining				)}'
																			)
																			self.label += F', {1/self.average_time_per_item:.2f} it/s'
												self.label += "]" if self.comment is None or len(self.comment				) == 0 else F', {self.comment}]'
												self.display()
					def 			lowerCamelCase__(				self       :List[str]				) ->   int:
												a__					=					html_progress_bar(self.value      ,self.total      ,self.prefix      ,self.label      ,self.width				)
												if self.parent is not None:
																			# If this is a child bar, the parent will take care of the display.
																			self.parent.display()
																			return
												if self.output is None:
																			a__					=					disp.display(disp.HTML(self.html_code				)      ,display_id=__snake_case				)
												else:
																			self.output.update(disp.HTML(self.html_code				)				)



					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Optional[int]:
												if self.parent is None and self.output is not None:
																			self.output.update(disp.HTML(''				)				)







class 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :List[str]      ,__snake_case       :Union[str, Any]      ,__snake_case       :Any=None				) ->   Optional[int]:
												super().__init__(__snake_case				)
												a__					=					None if column_names is None else [column_names]
												a__					=					None
					def 			lowerCamelCase__(				self       :List[str]				) ->   List[Any]:
												a__					=					html_progress_bar(self.value      ,self.total      ,self.prefix      ,self.label      ,self.width				)
												if self.inner_table is not None:
																			self.html_code += text_to_html_table(self.inner_table				)
												if self.child_bar is not None:
																			self.html_code += self.child_bar.html_code
												if self.output is None:
																			a__					=					disp.display(disp.HTML(self.html_code				)      ,display_id=__snake_case				)
												else:
																			self.output.update(disp.HTML(self.html_code				)				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :Union[str, Any]				) ->   Any:
												if self.inner_table is None:
																			a__					=					[list(values.keys()				), list(values.values()				)]
												else:
																			a__					=					self.inner_table[0]
																			if len(self.inner_table				) == 1:
																										# We give a chance to update the column names at the first iteration
																										for key in values.keys():
																																	if key not in columns:
																																								columns.append(__snake_case				)
																										a__					=					columns
																			self.inner_table.append([values[c] for c in columns]				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :Tuple      ,__snake_case       :List[str]=None      ,__snake_case       :str=3_00				) ->   List[str]:
												a__					=					NotebookProgressBar(__snake_case      ,prefix=__snake_case      ,parent=self      ,width=__snake_case				)
												return self.child_bar



					def 			lowerCamelCase__(				self       :Any				) ->   str:
												a__					=					None
												self.display()







class 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :int				) ->   Union[str, Any]:
												a__					=					None
												a__					=					None
												a__					=					False
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :int      ,__snake_case       :List[str]      ,__snake_case       :Any      ,**__snake_case       :Optional[Any]				) ->   List[str]:
												a__					=					'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
												a__					=					0
												a__					=					0
												a__					=					[self.first_column] + ['Training Loss']
												if args.evaluation_strategy != IntervalStrategy.NO:
																			column_names.append('Validation Loss'				)
												a__					=					NotebookTrainingTracker(state.max_steps      ,__snake_case				)
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :Optional[int]      ,__snake_case       :Any      ,__snake_case       :str      ,**__snake_case       :str				) ->   Union[str, Any]:
												a__					=					int(state.epoch				) if int(state.epoch				) == state.epoch else F'{state.epoch:.2f}'
												self.training_tracker.update(
												    state.global_step + 1      ,comment=F'Epoch {epoch}/{state.num_train_epochs}'      ,force_update=self._force_next_update      ,)
												a__					=					False
					def 			lowerCamelCase__(				self       :str      ,__snake_case       :List[str]      ,__snake_case       :Any      ,__snake_case       :List[Any]      ,__snake_case       :int=None      ,**__snake_case       :Union[str, Any]				) ->   Optional[Any]:
												if not has_length(__snake_case				):
																			return
												if self.prediction_bar is None:
																			if self.training_tracker is not None:
																										a__					=					self.training_tracker.add_child(len(__snake_case				)				)
																			else:
																										a__					=					NotebookProgressBar(len(__snake_case				)				)
																			self.prediction_bar.update(1				)
												else:
																			self.prediction_bar.update(self.prediction_bar.value + 1				)
					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :Optional[int]      ,__snake_case       :Dict      ,__snake_case       :str      ,**__snake_case       :Optional[int]				) ->   Any:
												if self.prediction_bar is not None:
																			self.prediction_bar.close()
												a__					=					None
					def 			lowerCamelCase__(				self       :str      ,__snake_case       :List[Any]      ,__snake_case       :Optional[int]      ,__snake_case       :Optional[int]      ,__snake_case       :Tuple=None      ,**__snake_case       :List[Any]				) ->   Union[str, Any]:
												# Only for when there is no evaluation
												if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
																			a__					=					{'Training Loss': logs['loss']}
																			# First column is necessarily Step sine we're not in epoch eval strategy
																			a__					=					state.global_step
																			self.training_tracker.write_line(__snake_case				)
					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :List[Any]      ,__snake_case       :Tuple      ,__snake_case       :List[str]      ,__snake_case       :str=None      ,**__snake_case       :Dict				) ->   Optional[Any]:
												if self.training_tracker is not None:
																			a__					=					{'Training Loss': 'No log', 'Validation Loss': 'No log'}
																			for log in reversed(state.log_history				):
																										if "loss" in log:
																																	a__					=					log['loss']
																																	break

																			if self.first_column == "Epoch":
																										a__					=					int(state.epoch				)
																			else:
																										a__					=					state.global_step
																			a__					=					'eval'
																			for k in metrics:
																										if k.endswith('_loss'				):
																																	a__					=					re.sub(R'\_loss$'      ,''      ,__snake_case				)
																			a__					=					metrics.pop('total_flos'      ,__snake_case				)
																			a__					=					metrics.pop('epoch'      ,__snake_case				)
																			a__					=					metrics.pop(F'{metric_key_prefix}_runtime'      ,__snake_case				)
																			a__					=					metrics.pop(F'{metric_key_prefix}_samples_per_second'      ,__snake_case				)
																			a__					=					metrics.pop(F'{metric_key_prefix}_steps_per_second'      ,__snake_case				)
																			a__					=					metrics.pop(F'{metric_key_prefix}_jit_compilation_time'      ,__snake_case				)
																			for k, v in metrics.items():
																										if k == F'{metric_key_prefix}_loss':
																																	a__					=					v
																										else:
																																	a__					=					k.split('_'				)
																																	a__					=					' '.join([part.capitalize() for part in splits[1:]]				)
																																	a__					=					v
																			self.training_tracker.write_line(__snake_case				)
																			self.training_tracker.remove_child()
																			a__					=					None
																			# Evaluation takes a long time so we should force the next update.
																			a__					=					True



					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :Optional[Any]      ,__snake_case       :Any      ,__snake_case       :Any      ,**__snake_case       :List[str]				) ->   Any:
												self.training_tracker.update(
												    state.global_step      ,comment=F'Epoch {int(state.epoch				)}/{state.num_train_epochs}'      ,force_update=__snake_case				)
												a__					=					None








import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case							:	Union[str, Any]					      =				logging.get_logger(__name__)

snake_case							:	Tuple					      =				torch.device('''cpu''')






def 							__lowercase       (			):
							a__					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
							a__					=					Image.open(requests.get(__lowerCAmelCase ,   stream=__lowerCAmelCase       ).raw       )
							return im






def 							__lowercase       (			__lowerCAmelCase							:    List[str]       ):
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]       )

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]       )

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]       )

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]       )






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    str       ):
							a__					=					dct.pop(__lowerCAmelCase       )
							a__					=					val






def 							__lowercase       (			__lowerCAmelCase							:    int       ):
							a__					=					[]
							for k in state_dict.keys():
														a__					=					k
														if ".pwconv" in k:
																					a__					=					k_new.replace('.pwconv' ,   '.point_wise_conv'       )
														if ".dwconv" in k:
																					a__					=					k_new.replace('.dwconv' ,   '.depth_wise_conv'       )
														if ".Proj." in k:
																					a__					=					k_new.replace('.Proj.' ,   '.proj.'       )
														if "patch_embed" in k_new:
																					a__					=					k_new.replace('patch_embed' ,   'swiftformer.patch_embed.patch_embedding'       )
														if "network" in k_new:
																					a__					=					k_new.split('.'       )
																					if ls[2].isdigit():
																												a__					=					'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]       )
																					else:
																												a__					=					k_new.replace('network' ,   'swiftformer.encoder.network'       )
														rename_keys.append((k, k_new)       )
							return rename_keys






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    str       ):
							a__					=					SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							a__					=					1_0_0_0
							a__					=					'huggingface/label-files'
							a__					=					'imagenet-1k-id2label.json'
							a__					=					json.load(open(hf_hub_download(__lowerCAmelCase ,   __lowerCAmelCase ,   repo_type='dataset'       ) ,   'r'       )       )
							a__					=					{int(__lowerCAmelCase       ): v for k, v in idalabel.items()}
							a__					=					idalabel
							a__					=					{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														a__					=					[3, 3, 6, 4]
														a__					=					[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														a__					=					[3, 3, 9, 6]
														a__					=					[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														a__					=					[4, 3, 1_0, 5]
														a__					=					[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														a__					=					[4, 4, 1_2, 6]
														a__					=					[6_4, 1_2_8, 3_2_0, 5_1_2]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith('https'       ):
																					a__					=					torch.hub.load_state_dict_from_url(__lowerCAmelCase ,   map_location='cpu' ,   check_hash=__lowerCAmelCase       )
														else:
																					a__					=					torch.load(__lowerCAmelCase ,   map_location='cpu'       )
							a__					=					checkpoint

							a__					=					create_rename_keys(__lowerCAmelCase       )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )

							# load HuggingFace model
							a__					=					SwiftFormerForImageClassification(__lowerCAmelCase       ).eval()
							hf_model.load_state_dict(__lowerCAmelCase       )

							# prepare test inputs
							a__					=					prepare_img()
							a__					=					ViTImageProcessor.from_pretrained('preprocessor_config'       )
							a__					=					processor(images=__lowerCAmelCase ,   return_tensors='pt'       )

							# compare outputs from both models
							a__					=					get_expected_output(__lowerCAmelCase       )
							a__					=					hf_model(inputs['pixel_values']       ).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]       )
							assert torch.allclose(hf_logits[0, 0:5] ,   __lowerCAmelCase ,   atol=1E-3       )

							Path(__lowerCAmelCase       ).mkdir(exist_ok=__lowerCAmelCase       )
							print(F'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'       )
							hf_model.save_pretrained(__lowerCAmelCase       )


if __name__ == "__main__":
						snake_case							:	int					      =				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--swiftformer_name''',
						    default='''swiftformer_xs''',
						    choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
						    type=str,
						    help='''Name of the SwiftFormer model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default='''./converted_outputs/''',
						    type=str,
						    help='''Path to the output PyTorch model directory.''',
						)
						parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

						snake_case							:	Optional[int]					      =				parser.parse_args()
						convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




