



"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
							import tensorflow as tf

							from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					List[Any]   	=				MBartConfig
		lowerCAmelCase			:					int   	=				{}
		lowerCAmelCase			:					Optional[int]   	=				"gelu"







		def __init__(	self						:  str			,_snake_case						:  Any			,_snake_case						:  Optional[int]=13			,_snake_case						:  str=7			,_snake_case						:  Any=True			,_snake_case						:  Union[str, Any]=False			,_snake_case						:  List[str]=99			,_snake_case						:  Any=32			,_snake_case						:  Optional[int]=2			,_snake_case						:  Dict=4			,_snake_case						:  str=37			,_snake_case						:  Optional[int]=0.1			,_snake_case						:  Dict=0.1			,_snake_case						:  List[str]=20			,_snake_case						:  Optional[Any]=2			,_snake_case						:  Optional[Any]=1			,_snake_case						:  Optional[int]=0			,)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    List[str]				=			parent
				lowercase__   :    Optional[Any]				=			batch_size
				lowercase__   :    Union[str, Any]				=			seq_length
				lowercase__   :    Tuple				=			is_training
				lowercase__   :    Optional[Any]				=			use_labels
				lowercase__   :    Any				=			vocab_size
				lowercase__   :    Tuple				=			hidden_size
				lowercase__   :    Dict				=			num_hidden_layers
				lowercase__   :    Optional[Any]				=			num_attention_heads
				lowercase__   :    Any				=			intermediate_size
				lowercase__   :    Any				=			hidden_dropout_prob
				lowercase__   :    Optional[int]				=			attention_probs_dropout_prob
				lowercase__   :    str				=			max_position_embeddings
				lowercase__   :    Union[str, Any]				=			eos_token_id
				lowercase__   :    List[Any]				=			pad_token_id
				lowercase__   :    Tuple				=			bos_token_id







		def 					UpperCAmelCase    (	self						:  int						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			ids_tensor([self.batch_size, self.seq_length - 1]			,self.vocab_size						)
				lowercase__   :    List[str]				=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)			,1						)
				lowercase__   :    Optional[int]				=			tf.concat([input_ids, eos_tensor]			,axis=1						)

				lowercase__   :    str				=			ids_tensor([self.batch_size, self.seq_length]			,self.vocab_size						)

				lowercase__   :    Union[str, Any]				=			self.config_cls(
				    vocab_size=self.vocab_size			,d_model=self.hidden_size			,encoder_layers=self.num_hidden_layers			,decoder_layers=self.num_hidden_layers			,encoder_attention_heads=self.num_attention_heads			,decoder_attention_heads=self.num_attention_heads			,encoder_ffn_dim=self.intermediate_size			,decoder_ffn_dim=self.intermediate_size			,dropout=self.hidden_dropout_prob			,attention_dropout=self.attention_probs_dropout_prob			,max_position_embeddings=self.max_position_embeddings			,eos_token_ids=[2]			,bos_token_id=self.bos_token_id			,pad_token_id=self.pad_token_id			,decoder_start_token_id=self.pad_token_id			,**self.config_updates			,)
				lowercase__   :    Tuple				=			prepare_mbart_inputs_dict(_snake_case			,_snake_case			,_snake_case						)
				return config, inputs_dict







		def 					UpperCAmelCase    (	self						:  int			,_snake_case						:  Dict			,_snake_case						:  Optional[Any]						)					->		str:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			TFMBartModel(config=_snake_case						).get_decoder()
				lowercase__   :    List[str]				=			inputs_dict['''input_ids''']

				lowercase__   :    Dict				=			input_ids[:1, :]
				lowercase__   :    Any				=			inputs_dict['''attention_mask'''][:1, :]
				lowercase__   :    List[Any]				=			inputs_dict['''head_mask''']
				lowercase__   :    List[Any]				=			1

				# first forward pass
				lowercase__   :    Any				=			model(_snake_case			,attention_mask=_snake_case			,head_mask=_snake_case			,use_cache=_snake_case						)

				lowercase__		,			lowercase__   :    int				=			outputs.to_tuple()
				lowercase__   :    Dict				=			past_key_values[1]





def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     )							->      int:
		if attention_mask is None:
				lowercase__   :    int				=			tf.cast(tf.math.not_equal(__lowerCamelCase						,     config.pad_token_id	)						,     tf.inta	)
		if decoder_attention_mask is None:
				lowercase__   :    str				=			tf.concat(
				    [
				        tf.ones(decoder_input_ids[:, :1].shape						,     dtype=tf.inta	),
				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,     config.pad_token_id	)						,     tf.inta	),
				    ]						,     axis=-1						,     )
		if head_mask is None:
				lowercase__   :    Optional[int]				=			tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
		if decoder_head_mask is None:
				lowercase__   :    List[Any]				=			tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
		if cross_attn_head_mask is None:
				lowercase__   :    Any				=			tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": decoder_attention_mask,
		    "head_mask": head_mask,
		    "decoder_head_mask": decoder_head_mask,
		    "cross_attn_head_mask": cross_attn_head_mask,
		}
@require_tf
class        __A       (	A_     ,A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					Optional[Any]   	=				(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
		lowerCAmelCase			:					Any   	=				(TFMBartForConditionalGeneration,) if is_tf_available() else ()
		lowerCAmelCase			:					Optional[Any]   	=				(
		    {
		        "conversational": TFMBartForConditionalGeneration,
		        "feature-extraction": TFMBartModel,
		        "summarization": TFMBartForConditionalGeneration,
		        "text2text-generation": TFMBartForConditionalGeneration,
		        "translation": TFMBartForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		lowerCAmelCase			:					Optional[int]   	=				True
		lowerCAmelCase			:					Optional[Any]   	=				False
		lowerCAmelCase			:					str   	=				False







		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  List[Any]			,_snake_case						:  Dict			,_snake_case						:  int			,_snake_case						:  List[Any]			,_snake_case						:  List[Any]						)					->		List[str]:






				"""simple docstring"""



				if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
						# Exception encountered when calling layer '...'
						return True

				return False







		def 					UpperCAmelCase    (	self						:  int						)					->		List[str]:






				"""simple docstring"""



				lowercase__   :    Dict				=			TFMBartModelTester(self						)
				lowercase__   :    Optional[Any]				=			ConfigTester(self			,config_class=_snake_case						)







		def 					UpperCAmelCase    (	self						:  Any						)					->		List[Any]:






				"""simple docstring"""



				self.config_tester.run_common_tests()







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*_snake_case						)





@require_sentencepiece
@require_tokenizers
@require_tf
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[Any]   	=				[
		    " UN Chief Says There Is No Military Solution in Syria",
		]
		lowerCAmelCase			:					Dict   	=				[
		    "Şeful ONU declară că nu există o soluţie militară în Siria",
		]
		lowerCAmelCase			:					str   	=				"facebook/mbart-large-en-ro"







		@cached_property
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[int]:






				"""simple docstring"""



				return AutoTokenizer.from_pretrained(self.model_name						)







		@cached_property
		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    Dict				=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
				return model







		def 					UpperCAmelCase    (	self						:  List[Any]			,**_snake_case						:  str						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    Dict				=			self.translate_src_text(**_snake_case						)
				self.assertListEqual(self.expected_text			,_snake_case						)







		def 					UpperCAmelCase    (	self						:  Any			,**_snake_case						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



				lowercase__   :    str				=			self.tokenizer(self.src_text			,**_snake_case			,return_tensors='''tf'''						)
				lowercase__   :    Dict				=			self.model.generate(
				    model_inputs.input_ids			,attention_mask=model_inputs.attention_mask			,num_beams=2						)
				lowercase__   :    List[str]				=			self.tokenizer.batch_decode(_snake_case			,skip_special_tokens=_snake_case						)
				return generated_words







		@slow
		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Union[str, Any]:






				"""simple docstring"""



				self._assert_generated_batch_equal_expected()





"""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()
lowerCAmelCase_   						=   logging.get_logger()



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase = True	)							->      Union[str, Any]:
		print(f"""Converting {name}..."""	)

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

				from_model.eval()
				lowercase__   :    Optional[int]				=			LevitForImageClassificationWithTeacher(__lowerCamelCase	).eval()
				lowercase__   :    str				=			OrderedDict()

				lowercase__   :    int				=			from_model.state_dict()
				lowercase__   :    Dict				=			list(from_model.state_dict().keys()	)
				lowercase__   :    Any				=			list(our_model.state_dict().keys()	)
				print(len(__lowerCamelCase	)						,     len(__lowerCamelCase	)	)
				for i in range(len(__lowerCamelCase	)	):
						lowercase__   :    str				=			weights[og_keys[i]]
				our_model.load_state_dict(__lowerCamelCase	)

				lowercase__   :    Optional[int]				=			torch.randn((2, 3, 2_24, 2_24)	)
				lowercase__   :    Optional[int]				=			from_model(__lowerCamelCase	)
				lowercase__   :    List[Any]				=			our_model(__lowerCamelCase	).logits

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

		lowercase__   :    Any				=			name
		print(__lowerCamelCase	)

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

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



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = None						,     __lowerCamelCase = True	)							->      List[Any]:
		lowercase__   :    Any				=			'''imagenet-1k-id2label.json'''
		lowercase__   :    Tuple				=			10_00
		lowercase__   :    Dict				=			(1, num_labels)

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

		lowercase__   :    Union[str, Any]				=			idalabel
		lowercase__   :    Optional[int]				=			{v: k for k, v in idalabel.items()}

		lowercase__   :    List[Any]				=			partial(__lowerCamelCase						,     num_labels=__lowerCamelCase						,     idalabel=__lowerCamelCase						,     labelaid=__lowerCamelCase	)

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

		lowercase__   :    Any				=			{
		    '''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]						,     __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__":
							lowerCAmelCase_   						=   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',
							)

							lowerCAmelCase_   						=   parser.parse_args()
							lowerCAmelCase_   						=   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)
