






"""simple docstring"""





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"umt5"
					UpperCAmelCase__								=				["past_key_values"]

					def __init__(			self  :      int     ,		__snake_case  :      Dict=2_5_0_1_1_2     ,		__snake_case  :      Dict=5_1_2     ,		__snake_case  :      List[Any]=6_4     ,		__snake_case  :      Union[str, Any]=1_0_2_4     ,		__snake_case  :      Optional[Any]=8     ,		__snake_case  :      Union[str, Any]=None     ,		__snake_case  :      Dict=6     ,		__snake_case  :      Any=3_2     ,		__snake_case  :      Any=1_2_8     ,		__snake_case  :      Optional[Any]=0.1     ,		__snake_case  :      Optional[int]=1E-6     ,		__snake_case  :      List[Any]=1.0     ,		__snake_case  :      Tuple="gated-gelu"     ,		__snake_case  :      Any=True     ,		__snake_case  :      Any=True     ,		__snake_case  :      Union[str, Any]="T5Tokenizer"     ,		__snake_case  :      Dict=True     ,		__snake_case  :      Union[str, Any]=0     ,		__snake_case  :      int=1     ,		__snake_case  :      Optional[Any]=0     ,		**__snake_case  :      Optional[int]     ,		)			->							str:
											super().__init__(
											    is_encoder_decoder=__snake_case     ,		tokenizer_class=__snake_case     ,		tie_word_embeddings=__snake_case     ,		pad_token_id=__snake_case     ,		eos_token_id=__snake_case     ,		decoder_start_token_id=__snake_case     ,		**__snake_case     ,		)
											__magic_name__:	List[Any]							=       vocab_size
											__magic_name__:	Dict							=       d_model
											__magic_name__:	List[Any]							=       d_kv
											__magic_name__:	Any							=       d_ff
											__magic_name__:	List[str]							=       num_layers
											__magic_name__:	Any							=       (
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											__magic_name__:	int							=       num_heads
											__magic_name__:	List[Any]							=       relative_attention_num_buckets
											__magic_name__:	List[str]							=       relative_attention_max_distance
											__magic_name__:	str							=       dropout_rate
											__magic_name__:	Optional[Any]							=       layer_norm_epsilon
											__magic_name__:	List[Any]							=       initializer_factor
											__magic_name__:	Union[str, Any]							=       feed_forward_proj
											__magic_name__:	int							=       use_cache

											__magic_name__:	int							=       self.feed_forward_proj.split("""-"""					)
											__magic_name__:	Tuple							=       act_info[-1]
											__magic_name__:	Dict							=       act_info[0] == """gated"""

											if len(__snake_case					) > 1 and act_info[0] != "gated" or len(__snake_case					) > 2:
																	raise ValueError(
																	    F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
																	    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
																	    """'gated-gelu' or 'relu'"""					)

											if feed_forward_proj == "gated-gelu":
																	__magic_name__:	Union[str, Any]							=       """gelu_new"""

					@property
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Dict:
											return self.d_model

					@property
					def   lowerCamelCase__   (			self  :      List[str]					)			->							Any:
											return self.num_heads






					@property
					def   lowerCamelCase__   (			self  :      Tuple					)			->							str:
											return self.num_layers



class       __A		(   SCREAMING_SNAKE_CASE_			):

					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
					def   lowerCamelCase__   (			self  :      List[str]					)			->							Mapping[str, Mapping[int, str]]:
											__magic_name__:	Optional[int]							=       {
											    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
											    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
											}
											if self.use_past:
																	__magic_name__:	str							=       """past_encoder_sequence + sequence"""
																	__magic_name__:	Any							=       {0: """batch"""}
																	__magic_name__:	List[Any]							=       {0: """batch""", 1: """past_decoder_sequence + sequence"""}
											else:
																	__magic_name__:	Union[str, Any]							=       {0: """batch""", 1: """decoder_sequence"""}
																	__magic_name__:	Tuple							=       {0: """batch""", 1: """decoder_sequence"""}

											if self.use_past:
																	self.fill_with_past_key_values_(__snake_case     ,		direction="""inputs"""					)

											return common_inputs

					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							int:
											return 1_3






					@property
					def   lowerCamelCase__   (			self  :      Dict					)			->							float:
											return 5E-4






'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:				Dict     	=			logging.get_logger()




def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					str		,lowerCAmelCase__:					LevitConfig		,lowerCAmelCase__:					Path		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							print(F"""Converting {name}..."""   )

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_128s'		,pretrained=lowerCAmelCase__   )
																					else:
																												SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_128'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 192:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_192'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 256:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_256'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 384:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_384'		,pretrained=lowerCAmelCase__   )

														from_model.eval()
														SCREAMING_SNAKE_CASE_				=				LevitForImageClassificationWithTeacher(lowerCAmelCase__   ).eval()
														SCREAMING_SNAKE_CASE_				=				OrderedDict()

														SCREAMING_SNAKE_CASE_				=				from_model.state_dict()
														SCREAMING_SNAKE_CASE_				=				list(from_model.state_dict().keys()   )
														SCREAMING_SNAKE_CASE_				=				list(our_model.state_dict().keys()   )
														print(len(lowerCAmelCase__   )		,len(lowerCAmelCase__   )   )
														for i in range(len(lowerCAmelCase__   )   ):
																					SCREAMING_SNAKE_CASE_				=				weights[og_keys[i]]
														our_model.load_state_dict(lowerCAmelCase__   )

														SCREAMING_SNAKE_CASE_				=				torch.randn((2, 3, 224, 224)   )
														SCREAMING_SNAKE_CASE_				=				from_model(lowerCAmelCase__   )
														SCREAMING_SNAKE_CASE_				=				our_model(lowerCAmelCase__   ).logits

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

							SCREAMING_SNAKE_CASE_				=				name
							print(lowerCAmelCase__   )

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name   )
														SCREAMING_SNAKE_CASE_				=				LevitImageProcessor()
														image_processor.save_pretrained(save_directory / checkpoint_name   )

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




def 							_UpperCamelCase				(   lowerCAmelCase__:					Path		,lowerCAmelCase__:					str = None		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				'imagenet-1k-id2label.json'
							SCREAMING_SNAKE_CASE_				=				1000
							SCREAMING_SNAKE_CASE_				=				(1, num_labels)

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

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

							SCREAMING_SNAKE_CASE_				=				partial(lowerCAmelCase__		,num_labels=lowerCAmelCase__		,idalabel=lowerCAmelCase__		,labelaid=lowerCAmelCase__   )

							SCREAMING_SNAKE_CASE_				=				{
							    'levit-128S': 128,
							    'levit-128': 128,
							    'levit-192': 192,
							    'levit-256': 256,
							    'levit-384': 384,
							}

							SCREAMING_SNAKE_CASE_				=				{
							    'levit-128S': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]		,num_attention_heads=[4, 6, 8]		,depths=[2, 3, 4]		,key_dim=[16, 16, 16]		,drop_path_rate=0		,),
							    'levit-128': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]		,num_attention_heads=[4, 8, 12]		,depths=[4, 4, 4]		,key_dim=[16, 16, 16]		,drop_path_rate=0		,),
							    'levit-192': ImageNetPreTrainedConfig(
							        hidden_sizes=[192, 288, 384]		,num_attention_heads=[3, 5, 6]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0		,),
							    'levit-256': ImageNetPreTrainedConfig(
							        hidden_sizes=[256, 384, 512]		,num_attention_heads=[4, 6, 8]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0		,),
							    'levit-384': ImageNetPreTrainedConfig(
							        hidden_sizes=[384, 512, 768]		,num_attention_heads=[6, 9, 12]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0.1		,),
							}

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				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 Levit* architecture,",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path",
							    default="levit-dump-folder/",
							    type=Path,
							    required=False,
							    help="Path to the output PyTorch model directory.",
							)
							parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
							parser.add_argument(
							    "--no-push_to_hub",
							    dest="push_to_hub",
							    action="store_false",
							    help="Do not push model and image processor to the hub",
							)

							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			parser.parse_args()
							SCREAMING_SNAKE_CASE							:				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)





