


"""simple docstring"""


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 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=None ,     lowerCamelCase__=None ,     lowerCamelCase__=None ,     lowerCamelCase__="resnet50" ,     lowerCamelCase__=3 ,     lowerCamelCase__=32 ,     lowerCamelCase__=3 ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     )      ->							int:
    lowercase__       :       Union[str, Any]				=						parent
    lowercase__       :       Union[str, Any]				=						out_indices if out_indices is not None else [4]
    lowercase__       :       Any				=						stage_names
    lowercase__       :       Optional[Any]				=						out_features
    lowercase__       :       int				=						backbone
    lowercase__       :       Optional[int]				=						batch_size
    lowercase__       :       Dict				=						image_size
    lowercase__       :       List[Any]				=						num_channels
    lowercase__       :       List[Any]				=						use_pretrained_backbone
    lowercase__       :       Dict				=						is_training


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       str				=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
    lowercase__       :       List[Any]				=						self.get_config()

    return config, pixel_values


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    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 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Optional[Any]:
    lowercase__       :       str				=						TimmBackbone(config=lowerCamelCase__						)
    model.to(lowerCamelCase__						)
    model.eval()
    with torch.no_grad():
     lowercase__       :       int				=						model(lowerCamelCase__						)
    self.parent.assertEqual(
        result.feature_map[-1].shape ,     (self.batch_size, model.channels[-1], 14, 14) ,     )







   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    lowercase__       :       Union[str, Any]				=						self.prepare_config_and_inputs()
    lowercase__	,       lowercase__       :       str				=						config_and_inputs
    lowercase__       :       Optional[int]				=						{"""pixel_values""": pixel_values}
    return config, inputs_dict

@require_torch
@require_timm
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   __UpperCAmelCase     ,   __UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Dict									=    (TimmBackbone,) if is_torch_available() else ()
   _a   :       str									=    {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
   _a   :       int									=    False
   _a   :       List[str]									=    False
   _a   :       Dict									=    False
   _a   :       Dict									=    False


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       List[str]				=						TimmBackboneModelTester(self						)
    lowercase__       :       List[str]				=						ConfigTester(self ,     config_class=lowerCamelCase__ ,     has_text_modality=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    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 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       str				=						"""resnet18"""
    lowercase__       :       Optional[Any]				=						"""microsoft/resnet-18"""

    lowercase__       :       List[str]				=						AutoBackbone.from_pretrained(lowerCamelCase__ ,     use_timm_backbone=lowerCamelCase__						)
    lowercase__       :       Any				=						AutoBackbone.from_pretrained(lowerCamelCase__						)

    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]						)

    lowercase__       :       Any				=						AutoBackbone.from_pretrained(lowerCamelCase__ ,     use_timm_backbone=lowerCamelCase__ ,     out_indices=[1, 2, 3]						)
    lowercase__       :       Dict				=						AutoBackbone.from_pretrained(lowerCamelCase__ ,     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 					UpperCAmelCase__(						self						)      ->							List[Any]:
    pass


   @unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""						)
   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    pass


   @unittest.skip("""TimmBackbone initialization is managed on the timm side"""						)
   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    pass


   @unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""						)
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    pass


   @unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""						)
   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    pass


   @unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint"""						)
   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    pass


   @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""						)
   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    pass


   @unittest.skip("""model weights aren't tied in TimmBackbone."""						)
   def 					UpperCAmelCase__(						self						)      ->							str:
    pass


   @unittest.skip("""model weights aren't tied in TimmBackbone."""						)
   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    pass


   @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""						)
   def 					UpperCAmelCase__(						self						)      ->							Any:
    pass


   @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""						)
   def 					UpperCAmelCase__(						self						)      ->							str:
    pass


   @unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration."""						)
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    pass


   @unittest.skip("""TimmBackbone doesn't support output_attentions."""						)
   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    pass


   @unittest.skip("""Safetensors is not supported by timm."""						)
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    pass


   @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""						)
   def 					UpperCAmelCase__(						self						)      ->							str:
    pass


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__	,       lowercase__       :       Tuple				=						self.model_tester.prepare_config_and_inputs_for_common()

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

     lowercase__       :       Optional[Any]				=						["""pixel_values"""]
     self.assertListEqual(arg_names[:1] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    lowercase__	,       lowercase__       :       Tuple				=						self.model_tester.prepare_config_and_inputs_for_common()
    lowercase__       :       List[str]				=						True
    lowercase__       :       Optional[int]				=						self.has_attentions

    # no need to test all models as different heads yield the same functionality
    lowercase__       :       List[str]				=						self.all_model_classes[0]
    lowercase__       :       Tuple				=						model_class(lowerCamelCase__						)
    model.to(lowerCamelCase__						)

    lowercase__       :       List[Any]				=						self._prepare_for_class(lowerCamelCase__ ,     lowerCamelCase__						)
    lowercase__       :       List[Any]				=						model(**lowerCamelCase__						)
    lowercase__       :       List[Any]				=						outputs[0][-1]

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

    if self.has_attentions:
     lowercase__       :       Optional[int]				=						outputs.attentions[0]
     attentions.retain_grad()

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

    self.assertIsNotNone(hidden_states.grad						)

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







   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__	,       lowercase__       :       Tuple				=						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     lowercase__       :       List[str]				=						model_class(lowerCamelCase__						)
     model.to(lowerCamelCase__						)
     model.eval()
     lowercase__       :       int				=						model(**lowerCamelCase__						)

     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
     lowercase__       :       Optional[int]				=						copy.deepcopy(lowerCamelCase__						)
     lowercase__       :       Optional[Any]				=						None
     lowercase__       :       Dict				=						model_class(lowerCamelCase__						)
     model.to(lowerCamelCase__						)
     model.eval()
     lowercase__       :       List[Any]				=						model(**lowerCamelCase__						)

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

     # Check backbone can be initialized with fresh weights
     lowercase__       :       Dict				=						copy.deepcopy(lowerCamelCase__						)
     lowercase__       :       Optional[Any]				=						False
     lowercase__       :       Union[str, Any]				=						model_class(lowerCamelCase__						)
     model.to(lowerCamelCase__						)
     model.eval()
     lowercase__       :       Any				=						model(**lowerCamelCase__						)



"""simple docstring"""


import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							Path       ,		lowerCamelCase__	:							str = None       ,		lowerCamelCase__	:							str = None       ,		lowerCamelCase__	:							str = None       ,		):
 if config_name_or_path is None:
  lowercase__       :       Tuple				=						"""facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

 if generator_tokenizer_name_or_path is None:
  lowercase__       :       List[Any]				=						generator_name_or_path

 if question_encoder_tokenizer_name_or_path is None:
  lowercase__       :       List[Any]				=						question_encoder_name_or_path

 lowercase__       :       Any				=						RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

 # Save model.
 lowercase__       :       Union[str, Any]				=						RagConfig.from_pretrained(lowerCamelCase__  )
 lowercase__       :       List[Any]				=						AutoConfig.from_pretrained(lowerCamelCase__  )
 lowercase__       :       Dict				=						AutoConfig.from_pretrained(lowerCamelCase__  )

 lowercase__       :       Union[str, Any]				=						gen_config
 lowercase__       :       Any				=						question_encoder_config

 lowercase__       :       str				=						model_class.from_pretrained_question_encoder_generator(
     lowerCamelCase__       ,		lowerCamelCase__       ,		config=lowerCamelCase__  )
 rag_model.save_pretrained(lowerCamelCase__  )

 # Sanity check.
 model_class.from_pretrained(lowerCamelCase__  )

 # Save tokenizers.
 lowercase__       :       Optional[Any]				=						AutoTokenizer.from_pretrained(lowerCamelCase__  )
 gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""  )
 lowercase__       :       Optional[int]				=						AutoTokenizer.from_pretrained(lowerCamelCase__  )
 question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""  )


if __name__ == "__main__":
   __snake_case    					=					argparse.ArgumentParser()
   parser.add_argument(
       '--model_type',
       choices=['rag_sequence', 'rag_token'],
       required=True,
       type=str,
       help='RAG model type: rag_sequence, rag_token',
   )
   parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
   parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
   parser.add_argument(
       '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
   )

   parser.add_argument(
       '--generator_tokenizer_name_or_path',
       type=str,
       help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
   )
   parser.add_argument(
       '--question_encoder_tokenizer_name_or_path',
       type=str,
       help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
   )
   parser.add_argument(
       '--config_name_or_path',
       type=str,
       help=(
           'Identifier of the model config to use, if not provided, resolves to a base config for a given'
           ' ``model_type``'
       ),
   )

   __snake_case    					=					parser.parse_args()

   __snake_case    					=					Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )