import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
  import torch

  from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  Optional[Any]		,  _lowercase					:  str		,  _lowercase					:  Tuple=None		,  _lowercase					:  Optional[int]=None		,  _lowercase					:  Optional[int]=None		,  _lowercase					:  str="resnet50"		,  _lowercase					:  Optional[int]=3		,  _lowercase					:  Dict=32		,  _lowercase					:  int=3		,  _lowercase					:  Any=True		,  _lowercase					:  Dict=True		,  ):



    """simple docstring"""



    _UpperCamelCase:     Dict							= parent
    _UpperCamelCase:     Tuple							= out_indices if out_indices is not None else [4]
    _UpperCamelCase:     int							= stage_names
    _UpperCamelCase:     int							= out_features
    _UpperCamelCase:     Optional[Any]							= backbone
    _UpperCamelCase:     int							= batch_size
    _UpperCamelCase:     Optional[Any]							= image_size
    _UpperCamelCase:     Tuple							= num_channels
    _UpperCamelCase:     Any							= use_pretrained_backbone
    _UpperCamelCase:     int							= is_training



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
    _UpperCamelCase:     Optional[int]							= self.get_config()

    return config, pixel_values



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    return TimmBackboneConfig(
        image_size=self.image_size		,  num_channels=self.num_channels		,  out_features=self.out_features		,  out_indices=self.out_indices		,  stage_names=self.stage_names		,  use_pretrained_backbone=self.use_pretrained_backbone		,  backbone=self.backbone		,  )



   def   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     int							= TimmBackbone(config=_lowercase							)
    model.to(_lowercase							)
    model.eval()
    with torch.no_grad():
     _UpperCamelCase:     str							= model(_lowercase							)
    self.parent.assertEqual(
        result.feature_map[-1].shape		,  (self.batch_size, model.channels[-1], 14, 14)		,  )



   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= self.prepare_config_and_inputs()
    _UpperCamelCase	,							_UpperCamelCase:     Any							= config_and_inputs
    _UpperCamelCase:     Dict							= {'''pixel_values''': pixel_values}
    return config, inputs_dict




@require_torch
@require_timm
class  __magic_name__  (		__a      ,	__a      ,	__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[Any]  			=   (TimmBackbone,) if is_torch_available() else ()
   lowerCAmelCase      :    Dict  			=   {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
   lowerCAmelCase      :    Any  			=   False
   lowerCAmelCase      :    Optional[Any]  			=   False
   lowerCAmelCase      :    int  			=   False
   lowerCAmelCase      :    Optional[Any]  			=   False



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= TimmBackboneModelTester(self							)
    _UpperCamelCase:     int							= ConfigTester(self		,  config_class=_lowercase		,  has_text_modality=_lowercase							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= '''resnet18'''
    _UpperCamelCase:     Optional[Any]							= '''microsoft/resnet-18'''

    _UpperCamelCase:     Tuple							= AutoBackbone.from_pretrained(_lowercase		,  use_timm_backbone=_lowercase							)
    _UpperCamelCase:     Tuple							= AutoBackbone.from_pretrained(_lowercase							)

    self.assertEqual(len(timm_model.out_features							)		,  len(transformers_model.out_features							)							)
    self.assertEqual(len(timm_model.stage_names							)		,  len(transformers_model.stage_names							)							)
    self.assertEqual(timm_model.channels		,  transformers_model.channels							)
    # Out indices are set to the last layer by default. For timm models, we don't know
    # the number of layers in advance, so we set it to (-1,), whereas for transformers
    # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
    self.assertEqual(timm_model.out_indices		,  (-1,)							)
    self.assertEqual(transformers_model.out_indices		,  [len(timm_model.stage_names							) - 1]							)

    _UpperCamelCase:     Optional[int]							= AutoBackbone.from_pretrained(_lowercase		,  use_timm_backbone=_lowercase		,  out_indices=[1, 2, 3]							)
    _UpperCamelCase:     Dict							= AutoBackbone.from_pretrained(_lowercase		,  out_indices=[1, 2, 3]							)

    self.assertEqual(timm_model.out_indices		,  transformers_model.out_indices							)
    self.assertEqual(len(timm_model.out_features							)		,  len(transformers_model.out_features							)							)
    self.assertEqual(timm_model.channels		,  transformers_model.channels							)



   @unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''							)
   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    pass



   @unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''							)
   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    pass



   @unittest.skip('''TimmBackbone initialization is managed on the timm side'''							)
   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    pass



   @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''							)
   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    pass



   @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''							)
   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    pass



   @unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''							)
   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    pass



   @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''							)
   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    pass



   @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''							)
   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    pass



   @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''							)
   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    pass



   @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''							)
   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    pass



   @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''							)
   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    pass



   @unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''							)
   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    pass



   @unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''							)
   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    pass



   @unittest.skip('''Safetensors is not supported by timm.'''							)
   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    pass



   @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   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase	,							_UpperCamelCase:     Dict							= self.model_tester.prepare_config_and_inputs_for_common()

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

     _UpperCamelCase:     List[Any]							= ['''pixel_values''']
     self.assertListEqual(arg_names[:1]		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase	,							_UpperCamelCase:     str							= self.model_tester.prepare_config_and_inputs_for_common()
    _UpperCamelCase:     int							= True
    _UpperCamelCase:     Union[str, Any]							= self.has_attentions

    # no need to test all models as different heads yield the same functionality
    _UpperCamelCase:     Dict							= self.all_model_classes[0]
    _UpperCamelCase:     Dict							= model_class(_lowercase							)
    model.to(_lowercase							)

    _UpperCamelCase:     str							= self._prepare_for_class(_lowercase		,  _lowercase							)
    _UpperCamelCase:     Optional[int]							= model(**_lowercase							)
    _UpperCamelCase:     Any							= outputs[0][-1]

    # Encoder-/Decoder-only models
    _UpperCamelCase:     Any							= outputs.hidden_states[0]
    hidden_states.retain_grad()

    if self.has_attentions:
     _UpperCamelCase:     List[str]							= outputs.attentions[0]
     attentions.retain_grad()

    output.flatten()[0].backward(retain_graph=_lowercase							)

    self.assertIsNotNone(hidden_states.grad							)

    if self.has_attentions:
     self.assertIsNotNone(attentions.grad							)



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase	,							_UpperCamelCase:     int							= self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     _UpperCamelCase:     Tuple							= model_class(_lowercase							)
     model.to(_lowercase							)
     model.eval()
     _UpperCamelCase:     Optional[Any]							= model(**_lowercase							)

     self.assertEqual(len(result.feature_maps							)		,  len(config.out_indices							)							)
     self.assertEqual(len(model.channels							)		,  len(config.out_indices							)							)

     # Check output of last stage is taken if out_features=None, out_indices=None
     _UpperCamelCase:     Any							= copy.deepcopy(_lowercase							)
     _UpperCamelCase:     List[Any]							= None
     _UpperCamelCase:     List[Any]							= model_class(_lowercase							)
     model.to(_lowercase							)
     model.eval()
     _UpperCamelCase:     Union[str, Any]							= model(**_lowercase							)

     self.assertEqual(len(result.feature_maps							)		,  1							)
     self.assertEqual(len(model.channels							)		,  1							)

     # Check backbone can be initialized with fresh weights
     _UpperCamelCase:     Tuple							= copy.deepcopy(_lowercase							)
     _UpperCamelCase:     int							= False
     _UpperCamelCase:     Optional[Any]							= model_class(_lowercase							)
     model.to(_lowercase							)
     model.eval()
     _UpperCamelCase:     List[str]							= model(**_lowercase							)
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration




def       lowerCAmelCase_     (		lowercase:							Any		)    ->				Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= [
     '''encoder.version''',
     '''decoder.version''',
     '''model.encoder.version''',
     '''model.decoder.version''',
     '''decoder.output_projection.weight''',
     '''_float_tensor''',
     '''encoder.embed_positions._float_tensor''',
     '''decoder.embed_positions._float_tensor''',
 ]
 for k in ignore_keys:
  state_dict.pop(lowercase  ,					lowercase		)




def       lowerCAmelCase_     (		lowercase:							int		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase	,							_UpperCamelCase:     Optional[int]							= emb.weight.shape
 _UpperCamelCase:     int							= nn.Linear(lowercase  ,					lowercase  ,					bias=lowercase		)
 _UpperCamelCase:     Any							= emb.weight.data
 return lin_layer




def       lowerCAmelCase_     (		lowercase:							Optional[Any]		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= torch.load(lowercase  ,					map_location='''cpu'''		)
 _UpperCamelCase:     Any							= mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
 _UpperCamelCase:     List[Any]							= mam_aaa['''model''']
 remove_ignore_keys_(lowercase		)
 _UpperCamelCase:     Tuple							= state_dict['''encoder.embed_tokens.weight'''].shape[0]

 _UpperCamelCase:     List[Any]							= MaMaaaConfig(
     vocab_size=lowercase  ,					max_position_embeddings=1_024  ,					encoder_layers=args.encoder_layers  ,					decoder_layers=args.decoder_layers  ,					encoder_attention_heads=args.encoder_attention_heads  ,					decoder_attention_heads=args.decoder_attention_heads  ,					encoder_ffn_dim=args.encoder_ffn_embed_dim  ,					decoder_ffn_dim=args.decoder_ffn_embed_dim  ,					d_model=args.encoder_embed_dim  ,					encoder_layerdrop=args.encoder_layerdrop  ,					decoder_layerdrop=args.decoder_layerdrop  ,					dropout=args.dropout  ,					attention_dropout=args.attention_dropout  ,					activation_dropout=args.activation_dropout  ,					activation_function='''relu'''  ,					)

 _UpperCamelCase:     List[Any]							= state_dict['''decoder.embed_tokens.weight''']
 _UpperCamelCase:     Optional[int]							= MaMaaaForConditionalGeneration(lowercase		)
 model.model.load_state_dict(lowercase  ,					strict=lowercase		)
 _UpperCamelCase:     Optional[Any]							= make_linear_from_emb(model.model.shared		)

 return model


if __name__ == "__main__":
  UpperCAmelCase_			    =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('''fairseq_path''', type=str, help='''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.''')
  UpperCAmelCase_			    =							parser.parse_args()
  UpperCAmelCase_			    =							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
  model.save_pretrained(args.pytorch_dump_folder_path)