



"""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  :						Optional[Any]    							=      logging.get_logger()



def        lowerCAmelCase_(   lowercase_     :							Any						,   lowercase_     :							Optional[int]						,   lowercase_     :							Optional[int]						,   lowercase_     :							Optional[Any]						,   lowercase_     :							Optional[Any] = True							)	->       List[str]:
				print(F"""Converting {name}..."""							)

				with torch.no_grad():
								if hidden_sizes == 1_28:
												if name[-1] == "S":
																_lowerCamelCase        =				timm.create_model('''levit_128s'''						,   pretrained=_snake_case							)
												else:
																_lowerCamelCase        =				timm.create_model('''levit_128'''						,   pretrained=_snake_case							)
								if hidden_sizes == 1_92:
												_lowerCamelCase        =				timm.create_model('''levit_192'''						,   pretrained=_snake_case							)
								if hidden_sizes == 2_56:
												_lowerCamelCase        =				timm.create_model('''levit_256'''						,   pretrained=_snake_case							)
								if hidden_sizes == 3_84:
												_lowerCamelCase        =				timm.create_model('''levit_384'''						,   pretrained=_snake_case							)

								from_model.eval()
								_lowerCamelCase        =				LevitForImageClassificationWithTeacher(_snake_case							).eval()
								_lowerCamelCase        =				OrderedDict()

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

								_lowerCamelCase        =				torch.randn((2, 3, 2_24, 2_24)							)
								_lowerCamelCase        =				from_model(_snake_case							)
								_lowerCamelCase        =				our_model(_snake_case							).logits

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

				_lowerCamelCase        =				name
				print(_snake_case							)

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

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



def        lowerCAmelCase_(   lowercase_     :							Tuple						,   lowercase_     :							Dict = None						,   lowercase_     :							int = True							)	->       Union[str, Any]:
				_lowerCamelCase        =				'''imagenet-1k-id2label.json'''
				_lowerCamelCase        =				10_00
				_lowerCamelCase        =				(1, num_labels)

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

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

				_lowerCamelCase        =				partial(_snake_case						,   num_labels=_snake_case						,   idalabel=_snake_case						,   labelaid=_snake_case							)

				_lowerCamelCase        =				{
				    '''levit-128S''': 1_28,
				    '''levit-128''': 1_28,
				    '''levit-192''': 1_92,
				    '''levit-256''': 2_56,
				    '''levit-384''': 3_84,
				}

				_lowerCamelCase        =				{
				    '''levit-128S''': ImageNetPreTrainedConfig(
				        hidden_sizes=[1_28, 2_56, 3_84]						,   num_attention_heads=[4, 6, 8]						,   depths=[2, 3, 4]						,   key_dim=[16, 16, 16]						,   drop_path_rate=0						,   ),
				    '''levit-128''': ImageNetPreTrainedConfig(
				        hidden_sizes=[1_28, 2_56, 3_84]						,   num_attention_heads=[4, 8, 12]						,   depths=[4, 4, 4]						,   key_dim=[16, 16, 16]						,   drop_path_rate=0						,   ),
				    '''levit-192''': ImageNetPreTrainedConfig(
				        hidden_sizes=[1_92, 2_88, 3_84]						,   num_attention_heads=[3, 5, 6]						,   depths=[4, 4, 4]						,   key_dim=[32, 32, 32]						,   drop_path_rate=0						,   ),
				    '''levit-256''': ImageNetPreTrainedConfig(
				        hidden_sizes=[2_56, 3_84, 5_12]						,   num_attention_heads=[4, 6, 8]						,   depths=[4, 4, 4]						,   key_dim=[32, 32, 32]						,   drop_path_rate=0						,   ),
				    '''levit-384''': ImageNetPreTrainedConfig(
				        hidden_sizes=[3_84, 5_12, 7_68]						,   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]						,   _snake_case						,   names_to_config[model_name]						,   _snake_case						,   _snake_case							)
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(names_to_hidden_sizes[model_name]						,   _snake_case						,   _snake_case						,   _snake_case						,   _snake_case							)
				return config, expected_shape


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      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  :						List[str]    							=      parser.parse_args()
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      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"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__SCREAMING_SNAKE_CASE  :						List[Any]    							=      {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						int    							=      [
			    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMForMultipleChoice''',
			    '''XLMForQuestionAnswering''',
			    '''XLMForQuestionAnsweringSimple''',
			    '''XLMForSequenceClassification''',
			    '''XLMForTokenClassification''',
			    '''XLMModel''',
			    '''XLMPreTrainedModel''',
			    '''XLMWithLMHeadModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      [
			    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMForMultipleChoice''',
			    '''TFXLMForQuestionAnsweringSimple''',
			    '''TFXLMForSequenceClassification''',
			    '''TFXLMForTokenClassification''',
			    '''TFXLMMainLayer''',
			    '''TFXLMModel''',
			    '''TFXLMPreTrainedModel''',
			    '''TFXLMWithLMHeadModel''',
			]


if TYPE_CHECKING:
			from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
			from .tokenization_xlm import XLMTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm import (
						    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMForMultipleChoice,
						    XLMForQuestionAnswering,
						    XLMForQuestionAnsweringSimple,
						    XLMForSequenceClassification,
						    XLMForTokenClassification,
						    XLMModel,
						    XLMPreTrainedModel,
						    XLMWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm import (
						    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMForMultipleChoice,
						    TFXLMForQuestionAnsweringSimple,
						    TFXLMForSequenceClassification,
						    TFXLMForTokenClassification,
						    TFXLMMainLayer,
						    TFXLMModel,
						    TFXLMPreTrainedModel,
						    TFXLMWithLMHeadModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						Tuple    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
