



'''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()
lowercase_      :				List[str]   					=    logging.get_logger()




@dataclass
class      __UpperCamelCase       :
	__A			  =					42
	__A			  =					field(default_factory=_UpperCAmelCase			)
	__A			  =					field(default_factory=_UpperCAmelCase			)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							len(list(m.modules()						)						) == 1 or isinstance(_lowerCAmelCase ,				nn.Convad						) or isinstance(_lowerCAmelCase ,				nn.BatchNormad						)
							if has_not_submodules:
													self.traced.append(_lowerCAmelCase						)







	def __call__(    self ,				_lowerCAmelCase						)    ->    Tuple:
							'''simple docstring'''






							for m in self.module.modules():
													self.handles.append(m.register_forward_hook(self._forward_hook						)						)
							self.module(_lowerCAmelCase						)
							[x.remove() for x in self.handles]
							return self







	@property
	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






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




@dataclass
class      __UpperCamelCase       :
	__A			  =					42
	__A			  =					42
	__A			  =					0
	__A			  =					field(default_factory=_UpperCAmelCase			)
	__A			  =					field(default_factory=_UpperCAmelCase			)







	def __call__(    self ,				_lowerCAmelCase						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							Tracker(self.dest						)(_lowerCAmelCase						).parametrized
							lowercase        =							Tracker(self.src						)(_lowerCAmelCase						).parametrized

							lowercase        =							list(filter(lambda _lowerCAmelCase						: type(_lowerCAmelCase						) not in self.src_skip ,				_lowerCAmelCase						)						)
							lowercase        =							list(filter(lambda _lowerCAmelCase						: type(_lowerCAmelCase						) not in self.dest_skip ,				_lowerCAmelCase						)						)

							if len(_lowerCAmelCase						) != len(_lowerCAmelCase						):
													raise Exception(
													    F"""Numbers of operations are different. Source module has {len(_lowerCAmelCase						)} operations while"""
													    F""" destination module has {len(_lowerCAmelCase						)}."""						)

							for dest_m, src_m in zip(_lowerCAmelCase ,				_lowerCAmelCase						):
													dest_m.load_state_dict(src_m.state_dict()						)
													if self.verbose == 1:
																			print(F"""Transfered from={src_m} to={dest_m}"""						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str ,  lowercase_    :      ResNetConfig ,  lowercase_    :      Path ,  lowercase_    :      bool = True						):
						print(F"""Converting {name}..."""						)
						with torch.no_grad():
												lowercase        =							timm.create_model(lowercase_ ,  pretrained=lowercase_						).eval()
												lowercase        =							ResNetForImageClassification(lowercase_						).eval()
												lowercase        =							ModuleTransfer(src=lowercase_ ,  dest=lowercase_						)
												lowercase        =							torch.randn((1, 3, 224, 224)						)
												module_transfer(lowercase_						)

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

						lowercase        =							F"""resnet{'-'.join(name.split('resnet'						)						)}"""
						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
												lowercase        =							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      SCREAMING_SNAKE_CASE				(				lowercase_    :      Path ,  lowercase_    :      str = None ,  lowercase_    :      bool = True						):
						lowercase        =							"""imagenet-1k-id2label.json"""
						lowercase        =							1000
						lowercase        =							(1, num_labels)

						lowercase        =							"""huggingface/label-files"""
						lowercase        =							num_labels
						lowercase        =							json.load(open(hf_hub_download(lowercase_ ,  lowercase_ ,  repo_type="""dataset"""						) ,  """r"""						)						)
						lowercase        =							{int(lowercase_						): v for k, v in idalabel.items()}

						lowercase        =							idalabel
						lowercase        =							{v: k for k, v in idalabel.items()}

						lowercase        =							partial(lowercase_ ,  num_labels=lowercase_ ,  idalabel=lowercase_ ,  labelaid=lowercase_						)

						lowercase        =							{
						    """resnet18""": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2] ,  hidden_sizes=[64, 128, 256, 512] ,  layer_type="""basic"""						),
						    """resnet26""": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2] ,  hidden_sizes=[256, 512, 1024, 2048] ,  layer_type="""bottleneck"""						),
						    """resnet34""": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3] ,  hidden_sizes=[64, 128, 256, 512] ,  layer_type="""basic"""						),
						    """resnet50""": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3] ,  hidden_sizes=[256, 512, 1024, 2048] ,  layer_type="""bottleneck"""						),
						    """resnet101""": ImageNetPreTrainedConfig(
						        depths=[3, 4, 23, 3] ,  hidden_sizes=[256, 512, 1024, 2048] ,  layer_type="""bottleneck"""						),
						    """resnet152""": ImageNetPreTrainedConfig(
						        depths=[3, 8, 36, 3] ,  hidden_sizes=[256, 512, 1024, 2048] ,  layer_type="""bottleneck"""						),
						}

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


if __name__ == "__main__":
						lowercase_      :				Tuple   					=    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.''',
						)

						lowercase_      :				int   					=    parser.parse_args()
						lowercase_      :				Path   					=    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)







'''simple docstring'''


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_      :				Optional[Any]   					=    logging.get_logger(__name__)

lowercase_      :				int   					=    {'''vocab_file''': '''spm_char.model'''}

lowercase_      :				int   					=    {
    '''vocab_file''': {
        '''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
        '''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
        '''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
    }
}

lowercase_      :				Optional[Any]   					=    {
    '''microsoft/speecht5_asr''': 1024,
    '''microsoft/speecht5_tts''': 1024,
    '''microsoft/speecht5_vc''': 1024,
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					VOCAB_FILES_NAMES
	__A			  =					PRETRAINED_VOCAB_FILES_MAP
	__A			  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A			  =					['''input_ids''', '''attention_mask''']







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase="<s>" ,				_lowerCAmelCase="</s>" ,				_lowerCAmelCase="<unk>" ,				_lowerCAmelCase="<pad>" ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    None:
							'''simple docstring'''






							lowercase        =							{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=_lowerCAmelCase ,				eos_token=_lowerCAmelCase ,				unk_token=_lowerCAmelCase ,				pad_token=_lowerCAmelCase ,				sp_model_kwargs=self.sp_model_kwargs ,				**_lowerCAmelCase ,				)

							lowercase        =							vocab_file

							lowercase        =							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(_lowerCAmelCase						)







	@property
	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							return self.sp_model.get_piece_size()







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							lowercase        =							{self.convert_ids_to_tokens(_lowerCAmelCase						): i for i in range(self.vocab_size						)}
							vocab.update(self.added_tokens_encoder						)
							return vocab







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






							lowercase        =							self.__dict__.copy()
							lowercase        =							None
							return state







	def __setstate__(    self ,				_lowerCAmelCase						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							d

							# for backward compatibility
							if not hasattr(self ,				"""sp_model_kwargs"""						):
													lowercase        =							{}

							lowercase        =							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(self.vocab_file						)







	def   _a   (    self ,				_lowerCAmelCase						)    ->    List[str]:
							'''simple docstring'''






							return self.sp_model.encode(_lowerCAmelCase ,				out_type=_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase						)    ->    List[Any]:
							'''simple docstring'''






							return self.sp_model.piece_to_id(_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase						)    ->    str:
							'''simple docstring'''






							lowercase        =							self.sp_model.IdToPiece(_lowerCAmelCase						)
							return token







	def   _a   (    self ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							[]
							lowercase        =							""""""
							for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																			out_string += self.sp_model.decode(_lowerCAmelCase						) + token
																			lowercase        =							[]
													else:
																			current_sub_tokens.append(_lowerCAmelCase						)
							out_string += self.sp_model.decode(_lowerCAmelCase						)
							return out_string.strip()







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    List[int]:
							'''simple docstring'''






							if token_ids_a is None:
													return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				_lowerCAmelCase = False						)    ->    List[int]:
							'''simple docstring'''






							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_lowerCAmelCase ,				token_ids_a=_lowerCAmelCase ,				already_has_special_tokens=_lowerCAmelCase						)

							lowercase        =							[1]
							if token_ids_a is None:
													return ([0] * len(_lowerCAmelCase						)) + suffix_ones
							return ([0] * len(_lowerCAmelCase						)) + ([0] * len(_lowerCAmelCase						)) + suffix_ones







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None						)    ->    Tuple[str]:
							'''simple docstring'''






							if not os.path.isdir(_lowerCAmelCase						):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
													return
							lowercase        =							os.path.join(
							    _lowerCAmelCase ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

							if os.path.abspath(self.vocab_file						) != os.path.abspath(_lowerCAmelCase						) and os.path.isfile(self.vocab_file						):
													copyfile(self.vocab_file ,				_lowerCAmelCase						)
							elif not os.path.isfile(self.vocab_file						):
													with open(_lowerCAmelCase ,				"""wb"""						) as fi:
																			lowercase        =							self.sp_model.serialized_model_proto()
																			fi.write(_lowerCAmelCase						)

							return (out_vocab_file,)


