

"""simple docstring"""






import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


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






@dataclass
class     _UpperCAmelCase			:
				__a :       nn.Module
				__a :       List[nn.Module]									=							field(default_factory=__a)
				__a :       list									=							field(default_factory=__a)
				def 							__snake_case (		self      ,		_A      ,		_A      ,		_A     )    ->    int:






					'''simple docstring'''







					_UpperCAmelCase    :							Union[str, Any]									=					len(list(m.modules()     )     ) == 1 or isinstance(_A      ,		nn.Convad     ) or isinstance(_A      ,		nn.BatchNormad     )
					if has_not_submodules:
						if not isinstance(_A      ,		_A     ):
							self.traced.append(_A     )
				def __call__(		self      ,		_A     )    ->    List[str]:






					'''simple docstring'''







					for m in self.module.modules():
						self.handles.append(m.register_forward_hook(self._forward_hook     )     )
					self.module(_A     )
					[x.remove() for x in self.handles]
					return self
				@property
				def 							__snake_case (		self     )    ->    List[str]:






					'''simple docstring'''







					return list(filter(lambda _A     : len(list(x.state_dict().keys()     )     ) > 0      ,		self.traced     )     )






@dataclass
class     _UpperCAmelCase			:
				__a :       nn.Module
				__a :       nn.Module
				__a :       int									=							0
				__a :       List									=							field(default_factory=__a)
				__a :       List									=							field(default_factory=__a)
				def __call__(		self      ,		_A     )    ->    int:






					'''simple docstring'''







					_UpperCAmelCase    :							Tuple									=					Tracker(self.dest     )(_A     ).parametrized
					_UpperCAmelCase    :							Dict									=					Tracker(self.src     )(_A     ).parametrized

					_UpperCAmelCase    :							List[Any]									=					list(filter(lambda _A     : type(_A     ) not in self.src_skip      ,		_A     )     )
					_UpperCAmelCase    :							List[str]									=					list(filter(lambda _A     : type(_A     ) not in self.dest_skip      ,		_A     )     )

					if len(_A     ) != len(_A     ):
						raise Exception(
						    f'''Numbers of operations are different. Source module has {len(_A     )} operations while'''
						    f''' destination module has {len(_A     )}.'''     )

					for dest_m, src_m in zip(_A      ,		_A     ):
						dest_m.load_state_dict(src_m.state_dict()     )
						if self.verbose == 1:
							print(f'''Transfered from={src_m} to={dest_m}'''     )
def       UpperCamelCase					(			_lowerCAmelCase     :						nn.Module,  _lowerCAmelCase     :						nn.Module )				->			nn.Module:
	# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
	_UpperCAmelCase    :							Dict									=					from_model.state_dict()
	_UpperCAmelCase    :							Optional[int]									=					our_model.state_dict()
	_UpperCAmelCase    :							Any									=					our_model.config
	_UpperCAmelCase    :							str									=					[]
	for stage_idx in range(len(config.hidden_sizes ) ):
		for block_id in range(config.depths[stage_idx] ):
			_UpperCAmelCase    :							Dict									=					f'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
			_UpperCAmelCase    :							List[Any]									=					f'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

			all_keys.append((from_key, to_key) )
			_UpperCAmelCase    :							int									=					f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
			_UpperCAmelCase    :							List[str]									=					f'''van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'''

			all_keys.append((from_key, to_key) )

	for from_key, to_key in all_keys:
		_UpperCAmelCase    :							int									=					from_state_dict.pop(_lowerCAmelCase )

	our_model.load_state_dict(_lowerCAmelCase )
	return our_model
def       UpperCamelCase					(			_lowerCAmelCase     :						str,  _lowerCAmelCase     :						VanConfig,  _lowerCAmelCase     :						str,  _lowerCAmelCase     :						nn.Module,  _lowerCAmelCase     :						Path,  _lowerCAmelCase     :						bool = True,  )				->			Union[str, Any]:
	print(f'''Downloading weights for {name}...''' )
	_UpperCAmelCase    :							Dict									=					cached_download(_lowerCAmelCase )
	print(f'''Converting {name}...''' )
	_UpperCAmelCase    :							Any									=					torch.load(_lowerCAmelCase )["""state_dict"""]
	from_model.load_state_dict(_lowerCAmelCase )
	from_model.eval()
	with torch.no_grad():
		_UpperCAmelCase    :							Dict									=					VanForImageClassification(_lowerCAmelCase ).eval()
		_UpperCAmelCase    :							List[str]									=					ModuleTransfer(src=_lowerCAmelCase,  dest=_lowerCAmelCase )
		_UpperCAmelCase    :							List[str]									=					torch.randn((1, 3, 224, 224) )
		module_transfer(_lowerCAmelCase )
		_UpperCAmelCase    :							Tuple									=					copy_parameters(_lowerCAmelCase,  _lowerCAmelCase )

	if not torch.allclose(from_model(_lowerCAmelCase ),  our_model(_lowerCAmelCase ).logits ):
		raise ValueError("""The model logits don't match the original one.""" )

	_UpperCAmelCase    :							Any									=					name
	print(_lowerCAmelCase )

	if push_to_hub:
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name,  commit_message="""Add model""",  use_temp_dir=_lowerCAmelCase,  )

		# we can use the convnext one
		_UpperCAmelCase    :							Dict									=					AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name,  commit_message="""Add image processor""",  use_temp_dir=_lowerCAmelCase,  )

		print(f'''Pushed {checkpoint_name}''' )
def       UpperCamelCase					(			_lowerCAmelCase     :						Path,  _lowerCAmelCase     :						str = None,  _lowerCAmelCase     :						bool = True )				->			int:
	_UpperCAmelCase    :							Optional[Any]									=					"""imagenet-1k-id2label.json"""
	_UpperCAmelCase    :							Union[str, Any]									=					1000

	_UpperCAmelCase    :							Optional[int]									=					"""huggingface/label-files"""
	_UpperCAmelCase    :							str									=					num_labels
	_UpperCAmelCase    :							List[str]									=					json.load(open(hf_hub_download(_lowerCAmelCase,  _lowerCAmelCase,  repo_type="""dataset""" ),  """r""" ) )
	_UpperCAmelCase    :							int									=					{int(_lowerCAmelCase ): v for k, v in idalabel.items()}

	_UpperCAmelCase    :							Dict									=					idalabel
	_UpperCAmelCase    :							List[Any]									=					{v: k for k, v in idalabel.items()}

	_UpperCAmelCase    :							List[str]									=					partial(_lowerCAmelCase,  num_labels=_lowerCAmelCase,  idalabel=_lowerCAmelCase,  labelaid=_lowerCAmelCase )

	_UpperCAmelCase    :							Dict									=					{
	    """van-tiny""": ImageNetPreTrainedConfig(
	        hidden_sizes=[32, 64, 160, 256],  depths=[3, 3, 5, 2],  mlp_ratios=[8, 8, 4, 4],  ),
	    """van-small""": ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 128, 320, 512],  depths=[2, 2, 4, 2],  mlp_ratios=[8, 8, 4, 4],  ),
	    """van-base""": ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 128, 320, 512],  depths=[3, 3, 12, 3],  mlp_ratios=[8, 8, 4, 4],  ),
	    """van-large""": ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 128, 320, 512],  depths=[3, 5, 27, 3],  mlp_ratios=[8, 8, 4, 4],  ),
	}

	_UpperCAmelCase    :							Optional[Any]									=					{
	    """van-tiny""": van_tiny,
	    """van-small""": van_small,
	    """van-base""": van_base,
	    """van-large""": van_large,
	}

	_UpperCAmelCase    :							List[Any]									=					{
	    """van-tiny""": (
	        """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
	    ),
	    """van-small""": (
	        """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
	    ),
	    """van-base""": (
	        """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
	    ),
	    """van-large""": (
	        """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
	    ),
	}

	if model_name:
		convert_weight_and_push(
		    _lowerCAmelCase,  names_to_config[model_name],  checkpoint=names_to_original_checkpoints[model_name],  from_model=names_to_original_models[model_name](),  save_directory=_lowerCAmelCase,  push_to_hub=_lowerCAmelCase,  )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(
			    _lowerCAmelCase,  _lowerCAmelCase,  checkpoint=names_to_original_checkpoints[model_name],  from_model=names_to_original_models[model_name](),  save_directory=_lowerCAmelCase,  push_to_hub=_lowerCAmelCase,  )


if __name__ == "__main__":
						lowerCamelCase__				:  str													=  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--model-name''',
						    default=None,
						    type=str,
						    help=(
						        '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
						        ''' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'''
						    ),
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default=None,
						    type=Path,
						    required=True,
						    help='''Path to the output PyTorch model directory.''',
						)
						parser.add_argument(
						    '''--van_dir''',
						    required=True,
						    type=Path,
						    help=(
						        '''A path to VAN\'s original implementation directory. You can download from here:'''
						        ''' https://github.com/Visual-Attention-Network/VAN-Classification'''
						    ),
						)
						parser.add_argument(
						    '''--push_to_hub''',
						    default=True,
						    type=bool,
						    required=False,
						    help='''If True, push model and image processor to the hub.''',
						)

						lowerCamelCase__				:  Union[str, Any]													=  parser.parse_args()
						lowerCamelCase__				:  Path													=  args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						lowerCamelCase__				:  Optional[int]													=  args.van_dir
						# append the path to the parents to maskformer dir
						sys.path.append(str(van_dir.parent))
						from van.models.van import van_base, van_large, van_small, van_tiny

						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)








"""simple docstring"""






import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


lowerCamelCase__				:  Any													=  torch.cuda.is_available()
lowerCamelCase__				:  Optional[Any]													=  [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCamelCase__				:  List[str]													=  ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCamelCase__				:  int													=  {
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1E-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}
def       UpperCamelCase					(			_lowerCAmelCase     :						Path,  _lowerCAmelCase     :						list )				->			str:
	_UpperCAmelCase    :							Dict									=					"""\n""".join(_lowerCAmelCase )
	Path(_lowerCAmelCase ).open("""w""" ).writelines(_lowerCAmelCase )
def       UpperCamelCase					(			_lowerCAmelCase     :						Optional[int] )				->			Optional[Any]:
	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(_lowerCAmelCase,  f'''{split}.source''' ),  _lowerCAmelCase )
		_dump_articles(os.path.join(_lowerCAmelCase,  f'''{split}.target''' ),  _lowerCAmelCase )
	return tmp_dir






class     _UpperCAmelCase			(		__a):
			@classmethod
			def 							__snake_case (		cls     )    ->    Optional[int]:






				'''simple docstring'''







				return cls
			@require_torch_multi_gpu
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[str]									=					{
				    """no_teacher""": True,
				    """freeze_encoder""": True,
				    """gpus""": 2,
				    """overwrite_output_dir""": True,
				    """sortish_sampler""": True,
				}
				self._test_distiller_cli_fork(_A      ,		check_contents=_A     )
			def 							__snake_case (		self      ,		_A      ,		_A=True     )    ->    int:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					{
				    """label_smoothing""": 0.0,
				    """early_stopping_patience""": -1,
				    """train_batch_size""": 1,
				    """eval_batch_size""": 2,
				    """max_epochs""": 2,
				    """alpha_mlm""": 0.2,
				    """alpha_ce""": 0.8,
				    """do_predict""": True,
				    """model_name_or_path""": """sshleifer/tinier_bart""",
				    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
				    """val_check_interval""": 0.5,
				}
				default_updates.update(_A     )
				_UpperCAmelCase    :							dict									=					CHEAP_ARGS.copy()
				_UpperCAmelCase    :							List[Any]									=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
				_UpperCAmelCase    :							Optional[Any]									=					self.get_auto_remove_tmp_dir()
				args_d.update(data_dir=_A      ,		output_dir=_A      ,		**_A     )

				def convert(_A      ,		_A     ):
					if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
						return ""
					if v is False or v is None:
						return ""
					if v is True:  # or len(str(v))==0:
						return f'''--{k}'''
					return f'''--{k}={v}'''

				_UpperCAmelCase    :							List[Any]									=					[x for x in (convert(_A      ,		_A     ) for k, v in args_d.items()) if len(_A     )]
				_UpperCAmelCase    :							Union[str, Any]									=					[sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
				execute_subprocess_async(_A      ,		env=self.get_env()     )

				_UpperCAmelCase    :							Union[str, Any]									=					os.listdir(_A     )
				_UpperCAmelCase    :							Dict									=					{os.path.basename(_A     ) for p in contents}
				_UpperCAmelCase    :							str									=					[p for p in contents if p.endswith("""ckpt"""     )]
				assert len(_A     ) > 0

				self.assertIn("""test_generations.txt"""      ,		_A     )
				self.assertIn("""test_results.txt"""      ,		_A     )

				# get the following from the module, (we don't have access to `model` here)
				_UpperCAmelCase    :							List[str]									=					os.path.join(_A      ,		"""metrics.json"""     )
				_UpperCAmelCase    :							List[Any]									=					"""rouge2"""

				_UpperCAmelCase    :							List[Any]									=					load_json(_A     )
				# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
				print(_A     )
				_UpperCAmelCase    :							str									=					metrics["""val"""][-1]
				self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]      ,		0.01     )
				self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']      ,		_A     )
				self.assertEqual(len(metrics["""test"""]     )      ,		1     )
				_UpperCAmelCase    :							Optional[int]									=					int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1     )
				self.assertEqual(len(metrics["""val"""]     )      ,		_A     )





