






'''simple docstring'''






import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
      import jax.numpy as jnp

      from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormModel,
      )




class a_    (    unittest.TestCase							):







   def __init__(self					,   __a					,   __a=1_3					,   __a=7					,   __a=True					,   __a=True					,   __a=True					,   __a=True					,   __a=9_9					,   __a=3_2					,   __a=5					,   __a=4					,   __a=3_7					,   __a="gelu"					,   __a=0.1					,   __a=0.1					,   __a=5_1_2					,   __a=1_6					,   __a=2					,   __a=0.02					,   __a=4					,   )			->      Dict:
      """simple docstring"""






      __snake_case				:      List[Any]     							= parent
      __snake_case				:      Union[str, Any]     							= batch_size
      __snake_case				:      int     							= seq_length
      __snake_case				:      Optional[int]     							= is_training
      __snake_case				:      Dict     							= use_attention_mask
      __snake_case				:      List[str]     							= use_token_type_ids
      __snake_case				:      Tuple     							= use_labels
      __snake_case				:      List[str]     							= vocab_size
      __snake_case				:      List[Any]     							= hidden_size
      __snake_case				:      Union[str, Any]     							= num_hidden_layers
      __snake_case				:      Union[str, Any]     							= num_attention_heads
      __snake_case				:      str     							= intermediate_size
      __snake_case				:      List[Any]     							= hidden_act
      __snake_case				:      Optional[Any]     							= hidden_dropout_prob
      __snake_case				:      List[str]     							= attention_probs_dropout_prob
      __snake_case				:      Tuple     							= max_position_embeddings
      __snake_case				:      Union[str, Any]     							= type_vocab_size
      __snake_case				:      Any     							= type_sequence_label_size
      __snake_case				:      int     							= initializer_range
      __snake_case				:      Tuple     							= num_choices







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






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

      __snake_case				:      List[Any]     							= None
      if self.use_attention_mask:
         __snake_case				:      List[str]     							= random_attention_mask([self.batch_size, self.seq_length])

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

      __snake_case				:      List[str]     							= RobertaPreLayerNormConfig(
          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					,   is_decoder=__a					,   initializer_range=self.initializer_range					,   )

      return config, input_ids, token_type_ids, attention_mask







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= self.prepare_config_and_inputs()
      __snake_case							,__snake_case							,__snake_case							,__snake_case				:      int     							= config_and_inputs

      __snake_case				:      int     							= True
      __snake_case				:      Union[str, Any]     							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
      __snake_case				:      Optional[Any]     							= ids_tensor([self.batch_size, self.seq_length]					,   vocab_size=2)

      return (
          config,
          input_ids,
          token_type_ids,
          encoder_hidden_states,
          encoder_attention_mask,
      )




@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							True

   _snake_case        =							(
       (
           FlaxRobertaPreLayerNormModel,
           FlaxRobertaPreLayerNormForCausalLM,
           FlaxRobertaPreLayerNormForMaskedLM,
           FlaxRobertaPreLayerNormForSequenceClassification,
           FlaxRobertaPreLayerNormForTokenClassification,
           FlaxRobertaPreLayerNormForMultipleChoice,
           FlaxRobertaPreLayerNormForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      str     							= FlaxRobertaPreLayerNormModelTester(self)







   @slow
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      for model_class_name in self.all_model_classes:
         __snake_case				:      str     							= model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,   from_pt=__a)
         __snake_case				:      List[Any]     							= model(np.ones((1, 1)))
         self.assertIsNotNone(__a)




@require_flax
class a_    (    unittest.TestCase							):







   @slow
   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      List[Any]     							= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,   from_pt=__a)

      __snake_case				:      Optional[int]     							= np.array([[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]]					,   dtype=jnp.intaa)
      __snake_case				:      List[str]     							= model(__a)[0]
      __snake_case				:      Dict     							= [1, 1_1, 5_0_2_6_5]
      self.assertEqual(list(output.shape)					,   __a)
      # compare the actual values for a slice.
      __snake_case				:      Optional[Any]     							= np.array(
          [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]					,   dtype=np.floataa)
      self.assertTrue(np.allclose(output[:, :3, :3]					,   __a					,   atol=1E-4))







   @slow
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,   from_pt=__a)

      __snake_case				:      Dict     							= np.array([[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]]					,   dtype=jnp.intaa)
      __snake_case				:      List[str]     							= model(__a)[0]
      # compare the actual values for a slice.
      __snake_case				:      Tuple     							= np.array(
          [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]					,   dtype=np.floataa)
      self.assertTrue(np.allclose(output[:, :3, :3]					,   __a					,   atol=1E-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()
__A    	=   logging.get_logger()




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       str  ,		A  :       LevitConfig  ,		A  :       Path  ,		A  :       bool = True  )    -> Dict:







   """simple docstring"""
   print(F"""Converting {name}..."""  )

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

      from_model.eval()
      __snake_case				:      str     							= LevitForImageClassificationWithTeacher(A  ).eval()
      __snake_case				:      int     							= OrderedDict()

      __snake_case				:      Optional[Any]     							= from_model.state_dict()
      __snake_case				:      Tuple     							= list(from_model.state_dict().keys()  )
      __snake_case				:      List[str]     							= list(our_model.state_dict().keys()  )
      print(len(A  )  ,		len(A  )  )
      for i in range(len(A  )  ):
         __snake_case				:      Optional[int]     							= weights[og_keys[i]]
      our_model.load_state_dict(A  )

      __snake_case				:      Tuple     							= torch.randn((2, 3, 2_24, 2_24)  )
      __snake_case				:      Union[str, Any]     							= from_model(A  )
      __snake_case				:      List[str]     							= our_model(A  ).logits

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

   __snake_case				:      int     							= name
   print(A  )

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

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




def        _SCREAMING_SNAKE_CASE  (				A  :       Path  ,		A  :       str = None  ,		A  :       bool = True  )    -> List[Any]:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= 'imagenet-1k-id2label.json'
   __snake_case				:      Tuple     							= 10_00
   __snake_case				:      Dict     							= (1, num_labels)

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

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

   __snake_case				:      Optional[int]     							= partial(A  ,		num_labels=A  ,		idalabel=A  ,		labelaid=A  )

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

   __snake_case				:      Union[str, 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]  ,		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__":
      __A    	=   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''',
      )

      __A    	=   parser.parse_args()
      __A    	=   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)