
'''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()
__lowercase							:				Optional[int]					=       logging.get_logger(__name__)

@dataclass
class 		__lowercase			:
			lowerCamelCase     :						nn.Module
			lowerCamelCase     :						List[nn.Module]		 =     field(default_factory=_lowercase      )
			lowerCamelCase     :						list		 =     field(default_factory=_lowercase      )

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A ):
								lowerCamelCase_   : 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 ):
								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     UpperCAmelCase__	(self ):
								# check the len of the state_dict keys to see if we have learnable params
								return list(filter(lambda A : len(list(x.state_dict().keys() ) ) > 0     ,				self.traced ) )

@dataclass
class 		__lowercase			:
			lowerCamelCase     :						nn.Module
			lowerCamelCase     :						nn.Module
			lowerCamelCase     :						int		 =     0
			lowerCamelCase     :						List		 =     field(default_factory=_lowercase      )
			lowerCamelCase     :						List		 =     field(default_factory=_lowercase      )

			def __call__(self     ,				A ):
								lowerCamelCase_   : Optional[int]       =						Tracker(self.dest )(A ).parametrized
								lowerCamelCase_   : int       =						Tracker(self.src )(A ).parametrized

								lowerCamelCase_   : Optional[Any]       =						list(filter(lambda A : type(A ) not in self.src_skip     ,				A ) )
								lowerCamelCase_   : Union[str, Any]       =						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 lowercase_		(				_lowercase     ,    _lowercase			)       ->							nn.Module:
					'''simple docstring'''

					lowerCamelCase_   : Tuple       =						from_model.state_dict()
					lowerCamelCase_   : Any       =						our_model.state_dict()
					lowerCamelCase_   : Dict       =						our_model.config
					lowerCamelCase_   : Union[str, Any]       =						[]
					for stage_idx in range(len(config.hidden_sizes			)			):
										for block_id in range(config.depths[stage_idx]			):
															lowerCamelCase_   : Optional[Any]       =						F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
															lowerCamelCase_   : List[str]       =						F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

															all_keys.append((from_key, to_key)			)
															lowerCamelCase_   : Optional[Any]       =						F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
															lowerCamelCase_   : Dict       =						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:
										lowerCamelCase_   : Optional[Any]       =						from_state_dict.pop(_lowercase			)

					our_model.load_state_dict(_lowercase			)
					return our_model




def lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase = True     ,    )       ->							Any:
					'''simple docstring'''

					print(F"""Downloading weights for {name}..."""			)
					lowerCamelCase_   : Tuple       =						cached_download(_lowercase			)
					print(F"""Converting {name}..."""			)
					lowerCamelCase_   : Optional[int]       =						torch.load(_lowercase			)['''state_dict''']
					from_model.load_state_dict(_lowercase			)
					from_model.eval()
					with torch.no_grad():
										lowerCamelCase_   : Tuple       =						VanForImageClassification(_lowercase			).eval()
										lowerCamelCase_   : Tuple       =						ModuleTransfer(src=_lowercase     ,    dest=_lowercase			)
										lowerCamelCase_   : Optional[int]       =						torch.randn((1, 3, 224, 224)			)
										module_transfer(_lowercase			)
										lowerCamelCase_   : Any       =						copy_parameters(_lowercase     ,    _lowercase			)

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

					lowerCamelCase_   : Tuple       =						name
					print(_lowercase			)

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

										# we can use the convnext one
										lowerCamelCase_   : Tuple       =						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=_lowercase     ,    )

										print(F"""Pushed {checkpoint_name}"""			)




def lowercase_		(				_lowercase     ,    _lowercase = None     ,    _lowercase = True			)       ->							Tuple:
					'''simple docstring'''

					lowerCamelCase_   : List[str]       =						'''imagenet-1k-id2label.json'''
					lowerCamelCase_   : Optional[Any]       =						1_000

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

					lowerCamelCase_   : Tuple       =						idalabel
					lowerCamelCase_   : List[str]       =						{v: k for k, v in idalabel.items()}

					lowerCamelCase_   : Optional[Any]       =						partial(_lowercase     ,    num_labels=_lowercase     ,    idalabel=_lowercase     ,    labelaid=_lowercase			)

					lowerCamelCase_   : List[str]       =						{
					    '''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]     ,    ),
					}

					lowerCamelCase_   : int       =						{
					    '''van-tiny''': van_tiny,
					    '''van-small''': van_small,
					    '''van-base''': van_base,
					    '''van-large''': van_large,
					}

					lowerCamelCase_   : Union[str, 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(
										    _lowercase     ,    names_to_config[model_name]     ,    checkpoint=names_to_original_checkpoints[model_name]     ,    from_model=names_to_original_models[model_name]()     ,    save_directory=_lowercase     ,    push_to_hub=_lowercase     ,    )
					else:
										for model_name, config in names_to_config.items():
															convert_weight_and_push(
															    _lowercase     ,    _lowercase     ,    checkpoint=names_to_original_checkpoints[model_name]     ,    from_model=names_to_original_models[model_name]()     ,    save_directory=_lowercase     ,    push_to_hub=_lowercase     ,    )


if __name__ == "__main__":
					__lowercase							:				Optional[Any]					=       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.''',
					)

					__lowercase							:				List[Any]					=       parser.parse_args()
					__lowercase							:				Path					=       args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					__lowercase							:				List[Any]					=       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 argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__lowercase							:				Optional[Any]					=       logging.getLogger(__name__)




def lowercase_		(				)       ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_   : Optional[int]       =						argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''			)
					parser.add_argument('''--model_name'''     ,    type=_lowercase     ,    default='''transfo-xl-wt103'''     ,    help='''pretrained model name'''			)
					parser.add_argument(
					    '''--split'''     ,    type=_lowercase     ,    default='''test'''     ,    choices=['''all''', '''valid''', '''test''']     ,    help='''which split to evaluate'''			)
					parser.add_argument('''--batch_size'''     ,    type=_lowercase     ,    default=10     ,    help='''batch size'''			)
					parser.add_argument('''--tgt_len'''     ,    type=_lowercase     ,    default=128     ,    help='''number of tokens to predict'''			)
					parser.add_argument('''--ext_len'''     ,    type=_lowercase     ,    default=0     ,    help='''length of the extended context'''			)
					parser.add_argument('''--mem_len'''     ,    type=_lowercase     ,    default=1_600     ,    help='''length of the retained previous heads'''			)
					parser.add_argument('''--clamp_len'''     ,    type=_lowercase     ,    default=1_000     ,    help='''max positional embedding index'''			)
					parser.add_argument('''--no_cuda'''     ,    action='''store_true'''     ,    help='''Do not use CUDA even though CUA is available'''			)
					parser.add_argument('''--work_dir'''     ,    type=_lowercase     ,    required=_lowercase     ,    help='''path to the work_dir'''			)
					parser.add_argument('''--no_log'''     ,    action='''store_true'''     ,    help='''do not log the eval result'''			)
					parser.add_argument('''--same_length'''     ,    action='''store_true'''     ,    help='''set same length attention with masking'''			)
					parser.add_argument('''--server_ip'''     ,    type=_lowercase     ,    default=''''''     ,    help='''Can be used for distant debugging.'''			)
					parser.add_argument('''--server_port'''     ,    type=_lowercase     ,    default=''''''     ,    help='''Can be used for distant debugging.'''			)
					lowerCamelCase_   : Dict       =						parser.parse_args()
					assert args.ext_len >= 0, "extended context length must be non-negative"

					if args.server_ip and args.server_port:
										# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
										import ptvsd

										print('''Waiting for debugger attach'''			)
										ptvsd.enable_attach(address=(args.server_ip, args.server_port)     ,    redirect_output=_lowercase			)
										ptvsd.wait_for_attach()

					lowerCamelCase_   : List[str]       =						torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
					logger.info('''device: {}'''.format(_lowercase			)			)

					# Load a pre-processed dataset
					# You can also build the corpus yourself using TransfoXLCorpus methods
					# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
					# and tokenizing the dataset
					# The pre-processed corpus is a convertion (using the conversion script )
					lowerCamelCase_   : str       =						TransfoXLCorpus.from_pretrained(args.model_name			)

					lowerCamelCase_   : Optional[int]       =						corpus.get_iterator('''valid'''     ,    args.batch_size     ,    args.tgt_len     ,    device=_lowercase     ,    ext_len=args.ext_len			)
					lowerCamelCase_   : str       =						corpus.get_iterator('''test'''     ,    args.batch_size     ,    args.tgt_len     ,    device=_lowercase     ,    ext_len=args.ext_len			)

					# Load a pre-trained model
					lowerCamelCase_   : str       =						TransfoXLLMHeadModel.from_pretrained(args.model_name			)
					model.to(_lowercase			)

					logger.info(
					    '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
					        args.batch_size     ,    args.tgt_len     ,    args.ext_len     ,    args.mem_len     ,    args.clamp_len			)			)

					model.reset_memory_length(args.mem_len			)
					if args.clamp_len > 0:
										lowerCamelCase_   : Union[str, Any]       =						args.clamp_len
					if args.same_length:
										lowerCamelCase_   : int       =						True

					###############################################################################
					# Evaluation code
					###############################################################################
					def evaluate(_lowercase			):
										# Turn on evaluation mode which disables dropout.
										model.eval()
										lowerCamelCase_, lowerCamelCase_   : Union[str, Any]       =						0, 0.0
										lowerCamelCase_   : Optional[int]       =						time.time()
										with torch.no_grad():
															lowerCamelCase_   : Dict       =						None
															for idx, (data, target, seq_len) in enumerate(_lowercase			):
																				lowerCamelCase_   : str       =						model(_lowercase     ,    lm_labels=_lowercase     ,    mems=_lowercase			)
																				lowerCamelCase_, lowerCamelCase_, lowerCamelCase_   : int       =						ret
																				lowerCamelCase_   : List[str]       =						loss.mean()
																				total_loss += seq_len * loss.item()
																				total_len += seq_len
															lowerCamelCase_   : List[str]       =						time.time() - start_time
										logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(_lowercase     ,    1_000 * total_time / (idx + 1)			)			)
										return total_loss / total_len

					# Run on test data.
					if args.split == "all":
										lowerCamelCase_   : Tuple       =						evaluate(_lowercase			)
										lowerCamelCase_   : List[Any]       =						evaluate(_lowercase			)
					elif args.split == "valid":
										lowerCamelCase_   : Union[str, Any]       =						evaluate(_lowercase			)
										lowerCamelCase_   : str       =						None
					elif args.split == "test":
										lowerCamelCase_   : Any       =						evaluate(_lowercase			)
										lowerCamelCase_   : int       =						None

					def format_log(_lowercase     ,    _lowercase			):
										lowerCamelCase_   : Optional[Any]       =						'''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(_lowercase     ,    _lowercase     ,    math.exp(_lowercase			)			)
										return log_str

					lowerCamelCase_   : List[Any]       =						''''''
					if valid_loss is not None:
										log_str += format_log(_lowercase     ,    '''valid'''			)
					if test_loss is not None:
										log_str += format_log(_lowercase     ,    '''test'''			)

					logger.info('''=''' * 100			)
					logger.info(_lowercase			)
					logger.info('''=''' * 100			)


if __name__ == "__main__":
					main()


