




'''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()
_lowercase  =			logging.get_logger()
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a    ,	a = True							):
						print(f'Converting {name}...'							)

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

												from_model.eval()
												__snake_case						 =   LevitForImageClassificationWithTeacher(a							).eval()
												__snake_case						 =   OrderedDict()

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

												__snake_case						 =   torch.randn((2, 3, 224, 224)							)
												__snake_case						 =   from_model(a							)
												__snake_case						 =   our_model(a							).logits

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

						__snake_case						 =   name
						print(a							)

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

												print(f'Pushed {checkpoint_name}'							)
def 						lowerCamelCase__   (				a    ,	a = None    ,	a = True							):
						__snake_case						 =   'imagenet-1k-id2label.json'
						__snake_case						 =   1000
						__snake_case						 =   (1, num_labels)

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

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

						__snake_case						 =   partial(a    ,	num_labels=a    ,	idalabel=a    ,	labelaid=a							)

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

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


if __name__ == "__main__":
	_lowercase  =			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""",
	)

	_lowercase  =			parser.parse_args()
	_lowercase  =			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 __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TFDebertaVaForMaskedLM,
	    TFDebertaVaForQuestionAnswering,
	    TFDebertaVaForSequenceClassification,
	    TFDebertaVaForTokenClassification,
	    TFDebertaVaModel,
	)
class 						a_				:




	def __init__(       self     :  Tuple       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Union[str, Any]=1_3       ,		__lowerCAmelCase     :  int=7       ,		__lowerCAmelCase     :  int=True       ,		__lowerCAmelCase     :  str=True       ,		__lowerCAmelCase     :  str=True       ,		__lowerCAmelCase     :  Optional[Any]=True       ,		__lowerCAmelCase     :  List[Any]=9_9       ,		__lowerCAmelCase     :  List[str]=3_2       ,		__lowerCAmelCase     :  List[Any]=2       ,		__lowerCAmelCase     :  Any=4       ,		__lowerCAmelCase     :  Any=3_7       ,		__lowerCAmelCase     :  Union[str, Any]="gelu"       ,		__lowerCAmelCase     :  Any=0.1       ,		__lowerCAmelCase     :  int=0.1       ,		__lowerCAmelCase     :  Optional[int]=5_1_2       ,		__lowerCAmelCase     :  Optional[Any]=1_6       ,		__lowerCAmelCase     :  Optional[Any]=2       ,		__lowerCAmelCase     :  Dict=0.02       ,		__lowerCAmelCase     :  Dict=False       ,		__lowerCAmelCase     :  int=True       ,		__lowerCAmelCase     :  Tuple="None"       ,		__lowerCAmelCase     :  Optional[int]=3       ,		__lowerCAmelCase     :  Optional[int]=4       ,		__lowerCAmelCase     :  Union[str, Any]=None       ,		):
							__snake_case						 =   parent
							__snake_case						 =   batch_size
							__snake_case						 =   seq_length
							__snake_case						 =   is_training
							__snake_case						 =   use_input_mask
							__snake_case						 =   use_token_type_ids
							__snake_case						 =   use_labels
							__snake_case						 =   vocab_size
							__snake_case						 =   hidden_size
							__snake_case						 =   num_hidden_layers
							__snake_case						 =   num_attention_heads
							__snake_case						 =   intermediate_size
							__snake_case						 =   hidden_act
							__snake_case						 =   hidden_dropout_prob
							__snake_case						 =   attention_probs_dropout_prob
							__snake_case						 =   max_position_embeddings
							__snake_case						 =   type_vocab_size
							__snake_case						 =   type_sequence_label_size
							__snake_case						 =   initializer_range
							__snake_case						 =   num_labels
							__snake_case						 =   num_choices
							__snake_case						 =   relative_attention
							__snake_case						 =   position_biased_input
							__snake_case						 =   pos_att_type
							__snake_case						 =   scope




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size      )

							__snake_case						 =   None
							if self.use_input_mask:
													__snake_case						 =   random_attention_mask([self.batch_size, self.seq_length]      )

							__snake_case						 =   None
							if self.use_token_type_ids:
													__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.type_vocab_size      )

							__snake_case						 =   None
							__snake_case						 =   None
							__snake_case						 =   None
							if self.use_labels:
													__snake_case						 =   ids_tensor([self.batch_size]       ,		self.type_sequence_label_size      )
													__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.num_labels      )

							__snake_case						 =   DebertaVaConfig(
							    vocab_size=self.vocab_size       ,		hidden_size=self.hidden_size       ,		num_hidden_layers=self.num_hidden_layers       ,		num_attention_heads=self.num_attention_heads       ,		intermediate_size=self.intermediate_size       ,		hidden_act=self.hidden_act       ,		hidden_dropout_prob=self.hidden_dropout_prob       ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,		max_position_embeddings=self.max_position_embeddings       ,		type_vocab_size=self.type_vocab_size       ,		relative_attention=self.relative_attention       ,		position_biased_input=self.position_biased_input       ,		initializer_range=self.initializer_range       ,		return_dict=__lowerCAmelCase       ,		)

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




	def   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]      ):
							__snake_case						 =   TFDebertaVaModel(config=__lowerCAmelCase      )
							__snake_case						 =   {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

							__snake_case						 =   [input_ids, input_mask]
							__snake_case						 =   model(__lowerCAmelCase      )

							__snake_case						 =   model(__lowerCAmelCase      )

							self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size)      )




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  List[Any]      ):
							__snake_case						 =   TFDebertaVaForMaskedLM(config=__lowerCAmelCase      )
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}
							__snake_case						 =   model(__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)      )




	def   lowercase__   (       self     :  Optional[int]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  List[Any]      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   TFDebertaVaForSequenceClassification(config=__lowerCAmelCase      )
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}

							__snake_case						 =   model(__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)      )




	def   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  int      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   TFDebertaVaForTokenClassification(config=__lowerCAmelCase      )
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}
							__snake_case						 =   model(__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.num_labels)      )




	def   lowercase__   (       self     :  int       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  List[Any]      ):
							__snake_case						 =   TFDebertaVaForQuestionAnswering(config=__lowerCAmelCase      )
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}

							__snake_case						 =   model(__lowerCAmelCase      )
							self.parent.assertEqual(result.start_logits.shape       ,		(self.batch_size, self.seq_length)      )
							self.parent.assertEqual(result.end_logits.shape       ,		(self.batch_size, self.seq_length)      )




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   self.prepare_config_and_inputs()
							(
							    (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       
							)						 =   config_and_inputs
							__snake_case						 =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict





@require_tf
class 						a_				(			UpperCAmelCase__     ,						UpperCAmelCase__     ,						unittest.TestCase						):
	lowercase_       :     List[str]			  =      (
	    (
	        TFDebertaVaModel,
	        TFDebertaVaForMaskedLM,
	        TFDebertaVaForQuestionAnswering,
	        TFDebertaVaForSequenceClassification,
	        TFDebertaVaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	lowercase_       :     Union[str, Any]			  =      (
	    {
	        '''feature-extraction''': TFDebertaVaModel,
	        '''fill-mask''': TFDebertaVaForMaskedLM,
	        '''question-answering''': TFDebertaVaForQuestionAnswering,
	        '''text-classification''': TFDebertaVaForSequenceClassification,
	        '''token-classification''': TFDebertaVaForTokenClassification,
	        '''zero-shot''': TFDebertaVaForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)

	lowercase_       :     int			  =      False
	lowercase_       :     List[Any]			  =      False




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   TFDebertaVaModelTester(self      )
							__snake_case						 =   ConfigTester(self       ,		config_class=__lowerCAmelCase       ,		hidden_size=3_7      )




	def   lowercase__   (       self     :  Dict      ):
							self.config_tester.run_common_tests()




	def   lowercase__   (       self     :  Optional[int]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase      )




	@slow
	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'      )
							self.assertIsNotNone(__lowerCAmelCase      )





@require_tf
class 						a_				(			unittest.TestCase						):




	@unittest.skip(reason='Model not available yet'      )
	def   lowercase__   (       self     :  List[str]      ):
							pass




	@slow
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'      )
							__snake_case						 =   tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]      )
							__snake_case						 =   tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
							__snake_case						 =   model(__lowerCAmelCase       ,		attention_mask=__lowerCAmelCase      )[0]

							__snake_case						 =   tf.constant(
							    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]      )
							tf.debugging.assert_near(output[:, 1:4, 1:4]       ,		__lowerCAmelCase       ,		atol=1E-4      )

