


import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import SwiftFormerForImageClassification, SwiftFormerModel
       from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor





class  lowerCAmelCase__:






    '''simple docstring'''





    def __init__(	self				:   Optional[Any]       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   List[str]=13       ,   __snake_case				:   Tuple=3       ,   __snake_case				:   List[str]=True       ,   __snake_case				:   List[str]=True       ,   __snake_case				:   Tuple=0.1       ,   __snake_case				:   Dict=0.1       ,   __snake_case				:   List[str]=224       ,   __snake_case				:   Union[str, Any]=1_000       ,   __snake_case				:   List[Any]=[3, 3, 6, 4]       ,   __snake_case				:   Union[str, Any]=[48, 56, 112, 220]       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   parent
       UpperCAmelCase_							:      str						=   batch_size
       UpperCAmelCase_							:      Union[str, Any]						=   num_channels
       UpperCAmelCase_							:      Tuple						=   is_training
       UpperCAmelCase_							:      Optional[int]						=   use_labels
       UpperCAmelCase_							:      Any						=   hidden_dropout_prob
       UpperCAmelCase_							:      Optional[int]						=   attention_probs_dropout_prob
       UpperCAmelCase_							:      int						=   num_labels
       UpperCAmelCase_							:      List[str]						=   image_size
       UpperCAmelCase_							:      List[Any]						=   layer_depths
       UpperCAmelCase_							:      Union[str, Any]						=   embed_dims





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[Any]						=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

       UpperCAmelCase_							:      List[str]						=   None
       if self.use_labels:
          UpperCAmelCase_							:      Dict						=   ids_tensor([self.batch_size]       ,   self.num_labels	)

       UpperCAmelCase_							:      List[Any]						=   self.get_config()

       return config, pixel_values, labels





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       return SwiftFormerConfig(
           depths=self.layer_depths       ,   embed_dims=self.embed_dims       ,   mlp_ratio=4       ,   downsamples=[True, True, True, True]       ,   hidden_act='''gelu'''       ,   num_labels=self.num_labels       ,   down_patch_size=3       ,   down_stride=2       ,   down_pad=1       ,   drop_rate=0.0       ,   drop_path_rate=0.0       ,   use_layer_scale=__snake_case       ,   layer_scale_init_value=1E-5       ,   )





    def 					_lowerCamelCase (	self				:   Optional[Any]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Any       ,   __snake_case				:   List[str]	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   SwiftFormerModel(config=__snake_case	)
       model.to(__snake_case	)
       model.eval()
       UpperCAmelCase_							:      List[Any]						=   model(__snake_case	)
       self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.embed_dims[-1], 7, 7)	)





    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   Optional[Any]       ,   __snake_case				:   Any       ,   __snake_case				:   List[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      str						=   self.num_labels
       UpperCAmelCase_							:      List[str]						=   SwiftFormerForImageClassification(__snake_case	)
       model.to(__snake_case	)
       model.eval()
       UpperCAmelCase_							:      List[Any]						=   model(__snake_case       ,   labels=__snake_case	)
       self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels)	)

       UpperCAmelCase_							:      Any						=   SwiftFormerForImageClassification(__snake_case	)
       model.to(__snake_case	)
       model.eval()

       UpperCAmelCase_							:      Optional[Any]						=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
       UpperCAmelCase_							:      List[str]						=   model(__snake_case	)
       self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels)	)





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       (UpperCAmelCase_)							:      Union[str, Any]						=   self.prepare_config_and_inputs()
       UpperCAmelCase_							:      Optional[Any]						=   {'''pixel_values''': pixel_values}
       return config, inputs_dict





@require_torch
class  lowerCAmelCase__(    snake_case__    ,    snake_case__    ,    unittest.TestCase						):






    '''simple docstring'''





    A_  :     List[str]			= (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    A_  :     Union[str, Any]			= (
        {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    A_  :     List[str]			= False
    A_  :     Optional[Any]			= False
    A_  :     List[Any]			= False
    A_  :     str			= False
    A_  :     int			= False





    def 					_lowerCamelCase (	self				:   List[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   SwiftFormerModelTester(self	)
       UpperCAmelCase_							:      List[Any]						=   ConfigTester(
           self       ,   config_class=__snake_case       ,   has_text_modality=__snake_case       ,   hidden_size=37       ,   num_attention_heads=12       ,   num_hidden_layers=12       ,   )





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       self.config_tester.run_common_tests()





    @unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''	)
    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       pass





    def 					_lowerCamelCase (	self				:   List[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          UpperCAmelCase_							:      Tuple						=   model_class(__snake_case	)
          UpperCAmelCase_							:      Optional[int]						=   model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(__snake_case       ,   nn.Linear	)	)





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          UpperCAmelCase_							:      List[str]						=   model_class(__snake_case	)
          UpperCAmelCase_							:      Optional[int]						=   inspect.signature(model.forward	)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          UpperCAmelCase_							:      Union[str, Any]						=   [*signature.parameters.keys()]

          UpperCAmelCase_							:      Union[str, Any]						=   ['''pixel_values''']
          self.assertListEqual(arg_names[:1]       ,   __snake_case	)





    def 					_lowerCamelCase (	self				:   List[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__snake_case	)





    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Dict						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__snake_case	)





    @slow
    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCAmelCase_							:      int						=   SwiftFormerModel.from_pretrained(__snake_case	)
          self.assertIsNotNone(__snake_case	)





    @unittest.skip(reason='''SwiftFormer does not output attentions'''	)
    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       pass





    def 					_lowerCamelCase (	self				:   str	):





       '''simple docstring'''
       def check_hidden_states_output(__snake_case				:   Optional[Any]       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Optional[int]	):
          UpperCAmelCase_							:      int						=   model_class(__snake_case	)
          model.to(__snake_case	)
          model.eval()

          with torch.no_grad():
             UpperCAmelCase_							:      List[Any]						=   model(**self._prepare_for_class(__snake_case       ,   __snake_case	)	)

          UpperCAmelCase_							:      Union[str, Any]						=   outputs.hidden_states

          UpperCAmelCase_							:      List[Any]						=   8
          self.assertEqual(len(__snake_case	)       ,   __snake_case	)  # TODO

          # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
          # with the width and height being successively divided by 2, after every 2 blocks
          for i in range(len(__snake_case	)	):
             self.assertEqual(
                 hidden_states[i].shape       ,   torch.Size(
                     [
                         self.model_tester.batch_size,
                         self.model_tester.embed_dims[i // 2],
                         (self.model_tester.image_size // 4) // 2 ** (i // 2),
                         (self.model_tester.image_size // 4) // 2 ** (i // 2),
                     ]	)       ,   )

       UpperCAmelCase_							:      Optional[int]						=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          UpperCAmelCase_							:      Tuple						=   True
          check_hidden_states_output(__snake_case       ,   __snake_case       ,   __snake_case	)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          UpperCAmelCase_							:      List[str]						=   True

          check_hidden_states_output(__snake_case       ,   __snake_case       ,   __snake_case	)





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       def _config_zero_init(__snake_case				:   Optional[Any]	):
          UpperCAmelCase_							:      str						=   copy.deepcopy(__snake_case	)
          for key in configs_no_init.__dict__.keys():
             if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                setattr(__snake_case       ,   __snake_case       ,   1E-10	)
             if isinstance(getattr(__snake_case       ,   __snake_case       ,   __snake_case	)       ,   __snake_case	):
                UpperCAmelCase_							:      List[Any]						=   _config_zero_init(getattr(__snake_case       ,   __snake_case	)	)
                setattr(__snake_case       ,   __snake_case       ,   __snake_case	)
          return configs_no_init

       UpperCAmelCase_							:      Any						=   self.model_tester.prepare_config_and_inputs_for_common()

       UpperCAmelCase_							:      List[str]						=   _config_zero_init(__snake_case	)
       for model_class in self.all_model_classes:
          UpperCAmelCase_							:      List[Any]						=   model_class(config=__snake_case	)
          for name, param in model.named_parameters():
             if param.requires_grad:
                self.assertIn(
                    ((param.data.mean() * 1E9) / 1E9).round().item()       ,   [0.0, 1.0]       ,   msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''       ,   )





    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       pass





def      snake_case_						( ):
   UpperCAmelCase_							:      str						=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
   return image





@require_torch
@require_vision
class  lowerCAmelCase__(    unittest.TestCase						):






    '''simple docstring'''





    @cached_property
    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''	) if is_vision_available() else None





    @slow
    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''	).to(__snake_case	)

       UpperCAmelCase_							:      List[str]						=   self.default_image_processor
       UpperCAmelCase_							:      Tuple						=   prepare_img()
       UpperCAmelCase_							:      Any						=   image_processor(images=__snake_case       ,   return_tensors='''pt'''	).to(__snake_case	)

       # forward pass
       with torch.no_grad():
          UpperCAmelCase_							:      str						=   model(**__snake_case	)

       # verify the logits
       UpperCAmelCase_							:      Dict						=   torch.Size((1, 1_000)	)
       self.assertEqual(outputs.logits.shape       ,   __snake_case	)

       UpperCAmelCase_							:      Tuple						=   torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]	).to(__snake_case	)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,   __snake_case       ,   atol=1E-4	)	)



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def      snake_case_						( __lowercase			,       __lowercase       ):
   # Load checkpoint
   UpperCAmelCase_							:      Tuple						=   torch.load(__lowercase			,       map_location='''cpu'''       )

   UpperCAmelCase_							:      Optional[int]						=   chkpt['''model''']

   # We have the base model one level deeper than the original XLM repository
   UpperCAmelCase_							:      str						=   {}
   for k, v in state_dict.items():
      if "pred_layer" in k:
         UpperCAmelCase_							:      Tuple						=   v
      else:
         UpperCAmelCase_							:      Union[str, Any]						=   v

   UpperCAmelCase_							:      int						=   chkpt['''params''']
   UpperCAmelCase_							:      Union[str, Any]						=   {n: v for n, v in config.items() if not isinstance(__lowercase			,       (torch.FloatTensor, numpy.ndarray)       )}

   UpperCAmelCase_							:      int						=   chkpt['''dico_word2id''']
   UpperCAmelCase_							:      List[Any]						=   {s + '''</w>''' if s.find('''@@'''       ) == -1 and i > 1_3 else s.replace('''@@'''			,       ''''''       ): i for s, i in vocab.items()}

   # Save pytorch-model
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + CONFIG_NAME
   UpperCAmelCase_							:      Dict						=   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

   print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''       )
   torch.save(__lowercase			,       __lowercase       )

   print(F'''Save configuration file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )

   print(F'''Save vocab file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )


if __name__ == "__main__":
       __UpperCamelCase				:  str      					=							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       __UpperCamelCase				:  Dict      					=							parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)