



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_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=13,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=224,			SCREAMING_SNAKE_CASE_=1000,			SCREAMING_SNAKE_CASE_=[3, 3, 6, 4],			SCREAMING_SNAKE_CASE_=[48, 56, 112, 220],			)     ->		Optional[Any]:
          UpperCamelCase    :  List[str]							=	parent
          UpperCamelCase    :  Tuple							=	batch_size
          UpperCamelCase    :  List[Any]							=	num_channels
          UpperCamelCase    :  int							=	is_training
          UpperCamelCase    :  List[str]							=	use_labels
          UpperCamelCase    :  int							=	hidden_dropout_prob
          UpperCamelCase    :  str							=	attention_probs_dropout_prob
          UpperCamelCase    :  Any							=	num_labels
          UpperCamelCase    :  Union[str, Any]							=	image_size
          UpperCamelCase    :  Any							=	layer_depths
          UpperCamelCase    :  int							=	embed_dims
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  int							=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

          UpperCamelCase    :  int							=	self.get_config()

          return config, pixel_values, labels
       def 					snake_case_       (			self       )     ->		int:
          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=SCREAMING_SNAKE_CASE_,			layer_scale_init_value=1e-5,			)
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Any:
          UpperCamelCase    :  str							=	SwiftFormerModel(config=SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()
          UpperCamelCase    :  str							=	model(SCREAMING_SNAKE_CASE_       )
          self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.embed_dims[-1], 7, 7)       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  Optional[Any]							=	self.num_labels
          UpperCamelCase    :  List[str]							=	SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()
          UpperCamelCase    :  str							=	model(SCREAMING_SNAKE_CASE_,			labels=SCREAMING_SNAKE_CASE_       )
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels)       )

          UpperCamelCase    :  Optional[Any]							=	SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()

          UpperCamelCase    :  Any							=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
          UpperCamelCase    :  Tuple							=	model(SCREAMING_SNAKE_CASE_       )
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels)       )






       def 					snake_case_       (			self       )     ->		Optional[int]:
          ((UpperCamelCase)	,     (UpperCamelCase)	,     (UpperCamelCase))    :  Optional[Any]							=	self.prepare_config_and_inputs()
          UpperCamelCase    :  Tuple							=	{'pixel_values': pixel_values}
          return config, inputs_dict






@require_torch
class 				lowerCAmelCase_		(  a__       ,    a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Optional[int]        =  (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
       UpperCAmelCase__				:					List[str]        =  (
           {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
           if is_torch_available()
           else {}
       )

       UpperCAmelCase__				:					Any        =  False
       UpperCAmelCase__				:					List[str]        =  False
       UpperCAmelCase__				:					List[Any]        =  False
       UpperCAmelCase__				:					List[Any]        =  False
       UpperCAmelCase__				:					Union[str, Any]        =  False
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Tuple							=	SwiftFormerModelTester(self       )
          UpperCamelCase    :  List[Any]							=	ConfigTester(
              self,			config_class=SCREAMING_SNAKE_CASE_,			has_text_modality=SCREAMING_SNAKE_CASE_,			hidden_size=37,			num_attention_heads=12,			num_hidden_layers=12,			)
       def 					snake_case_       (			self       )     ->		int:
          self.config_tester.run_common_tests()
       @unittest.skip(reason='SwiftFormer does not use inputs_embeds'       )
       def 					snake_case_       (			self       )     ->		int:
          pass
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase	,     UpperCamelCase    :  Union[str, Any]							=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase    :  Optional[int]							=	model_class(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  List[str]							=	model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_,			nn.Linear       )       )
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase	,     UpperCamelCase    :  Optional[int]							=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase    :  str							=	model_class(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Optional[Any]							=	inspect.signature(model.forward       )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase    :  str							=	[*signature.parameters.keys()]

             UpperCamelCase    :  Optional[int]							=	['pixel_values']
             self.assertListEqual(arg_names[:1],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  Tuple							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  List[str]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_       )
       @slow
       def 					snake_case_       (			self       )     ->		Tuple:
          for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase    :  List[str]							=	SwiftFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )
       @unittest.skip(reason='SwiftFormer does not output attentions'       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          pass
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          def check_hidden_states_output(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  Union[str, Any]							=	model_class(SCREAMING_SNAKE_CASE_       )
             model.to(SCREAMING_SNAKE_CASE_       )
             model.eval()

             with torch.no_grad():
                UpperCamelCase    :  str							=	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )       )

             UpperCamelCase    :  Dict							=	outputs.hidden_states

             UpperCamelCase    :  Optional[int]							=	8
             self.assertEqual(len(SCREAMING_SNAKE_CASE_       ),			SCREAMING_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(SCREAMING_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	,     UpperCamelCase    :  Optional[int]							=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase    :  Optional[Any]							=	True
             check_hidden_states_output(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCamelCase    :  int							=	True

             check_hidden_states_output(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		int:
          def _config_zero_init(SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  Union[str, Any]							=	copy.deepcopy(SCREAMING_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(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			1e-10       )
                if isinstance(getattr(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ),			SCREAMING_SNAKE_CASE_       ):
                   UpperCamelCase    :  Union[str, Any]							=	_config_zero_init(getattr(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )       )
                   setattr(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
             return configs_no_init

          UpperCamelCase	,     UpperCamelCase    :  str							=	self.model_tester.prepare_config_and_inputs_for_common()

          UpperCamelCase    :  int							=	_config_zero_init(SCREAMING_SNAKE_CASE_       )
          for model_class in self.all_model_classes:
             UpperCamelCase    :  List[str]							=	model_class(config=SCREAMING_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 					snake_case_       (			self       )     ->		Optional[int]:
          pass






def 		UpperCamelCase	(   )	->     int:
   UpperCamelCase    :  List[Any]							=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
   return image



@require_torch
@require_vision
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @cached_property
       def 					snake_case_       (			self       )     ->		Tuple:
          return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'       ) if is_vision_available() else None






       @slow
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  List[str]							=	SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'       ).to(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	self.default_image_processor
          UpperCamelCase    :  Union[str, Any]							=	prepare_img()
          UpperCamelCase    :  Dict							=	image_processor(images=SCREAMING_SNAKE_CASE_,			return_tensors='pt'       ).to(SCREAMING_SNAKE_CASE_       )

          # forward pass
          with torch.no_grad():
             UpperCamelCase    :  List[str]							=	model(**SCREAMING_SNAKE_CASE_       )

          # verify the logits
          UpperCamelCase    :  Dict							=	torch.Size((1, 1000)       )
          self.assertEqual(outputs.logits.shape,			SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]       ).to(SCREAMING_SNAKE_CASE_       )
          self.assertTrue(torch.allclose(outputs.logits[0, :3],			SCREAMING_SNAKE_CASE_,			atol=1e-4       )       )








import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


__UpperCAmelCase          =       ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
__UpperCAmelCase          =       {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
    raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       ''' Hello world! cécé herlolip'''

__UpperCAmelCase          =       [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]
def 		UpperCamelCase	(   snake_case__       :    Union[str, Any]	)	->     List[str]:
   UpperCamelCase    :  int							=	[
       'encoder.version',
       'decoder.version',
       'model.encoder.version',
       'model.decoder.version',
       '_float_tensor',
   ]
   for k in ignore_keys:
      state_dict.pop(snake_case__       ,     snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    int       ,     snake_case__       :    List[str]       ,     snake_case__       :    int	)	->     Any:
   UpperCamelCase    :  Dict							=	dct.pop(snake_case__	)
   UpperCamelCase    :  Optional[Any]							=	val
def 		UpperCamelCase	(   snake_case__       :    Dict	)	->     Tuple:
   UpperCamelCase    :  int							=	torch.load(snake_case__       ,     map_location='cpu'	)
   UpperCamelCase    :  Dict							=	torch.hub.load('pytorch/fairseq'       ,     'bart.large.cnn'	).eval()
   hub_interface.model.load_state_dict(sd['model']	)
   return hub_interface
def 		UpperCamelCase	(   snake_case__       :    List[str]	)	->     Dict:
   UpperCamelCase	,     UpperCamelCase    :  str							=	emb.weight.shape
   UpperCamelCase    :  Optional[int]							=	nn.Linear(snake_case__       ,     snake_case__       ,     bias=snake_case__	)
   UpperCamelCase    :  List[str]							=	emb.weight.data
   return lin_layer
@torch.no_grad()
def 		UpperCamelCase	(   snake_case__       :    Optional[int]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    str=None	)	->     Optional[Any]:
   if not os.path.exists(snake_case__	):
      UpperCamelCase    :  List[str]							=	torch.hub.load('pytorch/fairseq'       ,     snake_case__	).eval()
   else:
      UpperCamelCase    :  int							=	load_xsum_checkpoint(snake_case__	)

   bart.model.upgrade_state_dict(bart.model.state_dict()	)
   if hf_checkpoint_name is None:
      UpperCamelCase    :  Tuple							=	checkpoint_path.replace('.'       ,     '-'	)
   UpperCamelCase    :  Optional[int]							=	BartConfig.from_pretrained(snake_case__	)
   UpperCamelCase    :  Optional[Any]							=	bart.encode(snake_case__	).unsqueeze(0	)
   UpperCamelCase    :  Any							=	BartTokenizer.from_pretrained(snake_case__	).encode(snake_case__       ,     return_tensors='pt'	).unsqueeze(0	)
   if not torch.eq(snake_case__       ,     snake_case__	).all():
      raise ValueError(
          F"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""	)

   if checkpoint_path == "bart.large.mnli":
      UpperCamelCase    :  Union[str, Any]							=	bart.state_dict()
      remove_ignore_keys_(snake_case__	)
      UpperCamelCase    :  int							=	state_dict['model.decoder.embed_tokens.weight']
      for src, dest in mnli_rename_keys:
         rename_key(snake_case__       ,     snake_case__       ,     snake_case__	)
      UpperCamelCase    :  Any							=	BartForSequenceClassification(snake_case__	).eval()
      model.load_state_dict(snake_case__	)
      UpperCamelCase    :  Any							=	bart.predict('mnli'       ,     snake_case__       ,     return_logits=snake_case__	)
      UpperCamelCase    :  Tuple							=	model(snake_case__	)[0]  # logits
   else:  # no classification heads to worry about
      UpperCamelCase    :  List[str]							=	bart.model.state_dict()
      remove_ignore_keys_(snake_case__	)
      UpperCamelCase    :  List[str]							=	state_dict['decoder.embed_tokens.weight']
      UpperCamelCase    :  Union[str, Any]							=	bart.extract_features(snake_case__	)
      if hf_checkpoint_name == "facebook/bart-large":
         UpperCamelCase    :  List[str]							=	BartModel(snake_case__	).eval()
         model.load_state_dict(snake_case__	)
         UpperCamelCase    :  Optional[int]							=	model(snake_case__	).model[0]
      else:
         UpperCamelCase    :  Union[str, Any]							=	BartForConditionalGeneration(snake_case__	).eval()  # an existing summarization ckpt
         model.model.load_state_dict(snake_case__	)
         if hasattr(snake_case__       ,     'lm_head'	):
            UpperCamelCase    :  Optional[int]							=	make_linear_from_emb(model.model.shared	)
         UpperCamelCase    :  Dict							=	model.model(snake_case__	)[0]

    # Check results
   if fairseq_output.shape != new_model_outputs.shape:
      raise ValueError(
          F"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""	)
   if (fairseq_output != new_model_outputs).any().item():
      raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`'	)
   Path(snake_case__	).mkdir(exist_ok=snake_case__	)
   model.save_pretrained(snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
    )
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument(
        '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)



