






import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


snake_case__   :						Any													=   logging.getLogger(__name__)


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase=-1			)      ->			str:
     # in NER datasets, the last column is usually reserved for NER label
     UpperCamelCase_						=  label_idx




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[InputExample]:
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase			):
        UpperCamelCase_						=  mode.value
     UpperCamelCase_						=  os.path.join(_UpperCAmelCase		,		f"""{mode}.txt"""			)
     UpperCamelCase_						=  1
     UpperCamelCase_						=  []
     with open(_UpperCAmelCase		,		encoding='utf-8'			) as f:
        UpperCamelCase_						=  []
        UpperCamelCase_						=  []
        for line in f:
           if line.startswith('-DOCSTART-'			) or line == "" or line == "\n":
              if words:
                 examples.append(InputExample(guid=f"""{mode}-{guid_index}"""		,		words=_UpperCAmelCase		,		labels=_UpperCAmelCase			)			)
                 guid_index += 1
                 UpperCamelCase_						=  []
                 UpperCamelCase_						=  []
           else:
              UpperCamelCase_						=  line.split(' '			)
              words.append(splits[0]			)
              if len(_UpperCAmelCase			) > 1:
                 labels.append(splits[self.label_idx].replace('\n'		,		''			)			)
              else:
                 # Examples could have no label for mode = "test"
                 labels.append('O'			)
        if words:
           examples.append(InputExample(guid=f"""{mode}-{guid_index}"""		,		words=_UpperCAmelCase		,		labels=_UpperCAmelCase			)			)
     return examples




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Union[str, Any]:
     UpperCamelCase_						=  0
     for line in test_input_reader:
        if line.startswith('-DOCSTART-'			) or line == "" or line == "\n":
           writer.write(_UpperCAmelCase			)
           if not preds_list[example_id]:
              example_id += 1
        elif preds_list[example_id]:
           UpperCamelCase_						=  line.split()[0] + ' ' + preds_list[example_id].pop(0			) + '\n'
           writer.write(_UpperCAmelCase			)
        else:
           logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'		,		line.split()[0]			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[str]:
     if path:
        with open(_UpperCAmelCase		,		'r'			) as f:
           UpperCamelCase_						=  f.read().splitlines()
        if "O" not in labels:
           UpperCamelCase_						=  ['O'] + labels
        return labels
     else:
        return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self			)      ->			Optional[int]:
     # in CONLL2003 dataset chunk column is second-to-last
     super().__init__(label_idx=-2			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[str]:
     if path:
        with open(_UpperCAmelCase		,		'r'			) as f:
           UpperCamelCase_						=  f.read().splitlines()
        if "O" not in labels:
           UpperCamelCase_						=  ['O'] + labels
        return labels
     else:
        return [
            "O",
            "B-ADVP",
            "B-INTJ",
            "B-LST",
            "B-PRT",
            "B-NP",
            "B-SBAR",
            "B-VP",
            "B-ADJP",
            "B-CONJP",
            "B-PP",
            "I-ADVP",
            "I-INTJ",
            "I-LST",
            "I-PRT",
            "I-NP",
            "I-SBAR",
            "I-VP",
            "I-ADJP",
            "I-CONJP",
            "I-PP",
        ]




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[InputExample]:
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase			):
        UpperCamelCase_						=  mode.value
     UpperCamelCase_						=  os.path.join(_UpperCAmelCase		,		f"""{mode}.txt"""			)
     UpperCamelCase_						=  1
     UpperCamelCase_						=  []

     with open(_UpperCAmelCase		,		encoding='utf-8'			) as f:
        for sentence in parse_incr(_UpperCAmelCase			):
           UpperCamelCase_						=  []
           UpperCamelCase_						=  []
           for token in sentence:
              words.append(token['form']			)
              labels.append(token['upos']			)
           assert len(_UpperCAmelCase			) == len(_UpperCAmelCase			)
           if words:
              examples.append(InputExample(guid=f"""{mode}-{guid_index}"""		,		words=_UpperCAmelCase		,		labels=_UpperCAmelCase			)			)
              guid_index += 1
     return examples




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			str:
     UpperCamelCase_						=  0
     for sentence in parse_incr(_UpperCAmelCase			):
        UpperCamelCase_						=  preds_list[example_id]
        UpperCamelCase_						=  ''
        for token in sentence:
           out += f"""{token["form"]} ({token["upos"]}|{s_p.pop(0			)}) """
        out += "\n"
        writer.write(_UpperCAmelCase			)
        example_id += 1




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[str]:
     if path:
        with open(_UpperCAmelCase		,		'r'			) as f:
           return f.read().splitlines()
     else:
        return [
            "ADJ",
            "ADP",
            "ADV",
            "AUX",
            "CCONJ",
            "DET",
            "INTJ",
            "NOUN",
            "NUM",
            "PART",
            "PRON",
            "PROPN",
            "PUNCT",
            "SCONJ",
            "SYM",
            "VERB",
            "X",
        ]






'''simple docstring'''







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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_      	=	logging.get_logger()




@dataclass
class _SCREAMING_SNAKE_CASE:
						A_			:							nn.Module
						A_			:							List[nn.Module]     		= field(default_factory=_SCREAMING_SNAKE_CASE					)
						A_			:							list     		= field(default_factory=_SCREAMING_SNAKE_CASE					)




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       Tensor							,							UpperCamelCase_ :       Tensor      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = len(list(m.modules()      )      ) == 1 or isinstance(UpperCamelCase_							,							nn.Convad      ) or isinstance(UpperCamelCase_							,							nn.BatchNormad      )
													if has_not_submodules:
																				self.traced.append(UpperCamelCase_      )




						def __call__(    self :       Tuple							,							UpperCamelCase_ :       Tensor      )     ->				int:
													for m in self.module.modules():
																				self.handles.append(m.register_forward_hook(self._forward_hook      )      )
													self.module(UpperCamelCase_      )
													[x.remove() for x in self.handles]
													return self




						@property
						def      __lowerCamelCase       (    self :       Tuple      )     ->				str:
													# check the len of the state_dict keys to see if we have learnable params
													return list(filter(lambda UpperCamelCase_      : len(list(x.state_dict().keys()      )      ) > 0							,							self.traced      )      )




@dataclass
class _SCREAMING_SNAKE_CASE:
						A_			:							nn.Module
						A_			:							nn.Module
						A_			:							int     		= 0
						A_			:							List     		= field(default_factory=_SCREAMING_SNAKE_CASE					)
						A_			:							List     		= field(default_factory=_SCREAMING_SNAKE_CASE					)




						def __call__(    self :       List[Any]							,							UpperCamelCase_ :       Tensor      )     ->				int:
													SCREAMING_SNAKE_CASE__     :List[str]						    = Tracker(self.dest      )(UpperCamelCase_      ).parametrized
													SCREAMING_SNAKE_CASE__     :Any						    = Tracker(self.src      )(UpperCamelCase_      ).parametrized

													SCREAMING_SNAKE_CASE__     :Dict						    = list(filter(lambda UpperCamelCase_      : type(UpperCamelCase_      ) not in self.src_skip							,							UpperCamelCase_      )      )
													SCREAMING_SNAKE_CASE__     :str						    = list(filter(lambda UpperCamelCase_      : type(UpperCamelCase_      ) not in self.dest_skip							,							UpperCamelCase_      )      )

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

													for dest_m, src_m in zip(UpperCamelCase_							,							UpperCamelCase_      ):
																				dest_m.load_state_dict(src_m.state_dict()      )
																				if self.verbose == 1:
																											print(f'''Transfered from={src_m} to={dest_m}'''      )


def    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  ResNetConfig				,						UpperCAmelCase__				:  Path				,						UpperCAmelCase__				:  bool = True		)			->	Union[str, Any]:


							'''simple docstring'''






							print(F'''Converting {name}...'''		)
							with torch.no_grad():
														SCREAMING_SNAKE_CASE__     :str						    = timm.create_model(UpperCAmelCase__				,						pretrained=UpperCAmelCase__		).eval()
														SCREAMING_SNAKE_CASE__     :List[str]						    = ResNetForImageClassification(UpperCAmelCase__		).eval()
														SCREAMING_SNAKE_CASE__     :Dict						    = ModuleTransfer(src=UpperCAmelCase__				,						dest=UpperCAmelCase__		)
														SCREAMING_SNAKE_CASE__     :List[Any]						    = torch.randn((1, 3, 2_2_4, 2_2_4)		)
														module_transfer(UpperCAmelCase__		)

							assert torch.allclose(from_model(UpperCAmelCase__		)				,						our_model(UpperCAmelCase__		).logits		), "The model logits don't match the original one."

							SCREAMING_SNAKE_CASE__     :List[str]						    = F'''resnet{"-".join(name.split("resnet"		)		)}'''
							print(UpperCAmelCase__		)

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

														# we can use the convnext one
														SCREAMING_SNAKE_CASE__     :int						    = 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=UpperCAmelCase__				,						)

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


def    lowerCamelCase       (			UpperCAmelCase__				:  Path				,						UpperCAmelCase__				:  str = None				,						UpperCAmelCase__				:  bool = True		)			->	int:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[str]						    = 'imagenet-1k-id2label.json'
							SCREAMING_SNAKE_CASE__     :Any						    = 1_0_0_0
							SCREAMING_SNAKE_CASE__     :List[Any]						    = (1, num_labels)

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

							SCREAMING_SNAKE_CASE__     :str						    = idalabel
							SCREAMING_SNAKE_CASE__     :Any						    = {v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = partial(UpperCAmelCase__				,						num_labels=UpperCAmelCase__				,						idalabel=UpperCAmelCase__				,						labelaid=UpperCAmelCase__		)

							SCREAMING_SNAKE_CASE__     :List[str]						    = {
							    'resnet18': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]				,						hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]				,						layer_type='basic'		),
							    'resnet26': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet34': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]				,						hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]				,						layer_type='basic'		),
							    'resnet50': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet101': ImageNetPreTrainedConfig(
							        depths=[3, 4, 2_3, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet152': ImageNetPreTrainedConfig(
							        depths=[3, 8, 3_6, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							}

							if model_name:
														convert_weight_and_push(UpperCAmelCase__				,						names_to_config[model_name]				,						UpperCAmelCase__				,						UpperCAmelCase__		)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__		)
							return config, expected_shape


if __name__ == "__main__":
		UpperCamelCase_      	=	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: resnet18,26,34,50,101,152. 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(
		    '''--push_to_hub''',
		    default=True,
		    type=bool,
		    required=False,
		    help='''If True, push model and image processor to the hub.''',
		)

		UpperCamelCase_      	=	parser.parse_args()
		UpperCamelCase_      	=	args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






