



'''simple docstring'''





import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor






class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  def __init__( self      :	List[str]			,	_a      :	Dict			,	_a      :	Tuple=3			,	_a      :	List[str]=32			,	_a      :	Optional[Any]=3			,	_a      :	Union[str, Any]=10			,	_a      :	Optional[Any]=[10, 20, 30, 40]			,	_a      :	Any=[1, 1, 2, 1]			,	_a      :	Any=True			,	_a      :	Optional[Any]=True			,	_a      :	str="relu"			,	_a      :	int=3			,	_a      :	Dict=None			,	)					->					Optional[Any]:
    __lowerCamelCase       :       List[str]           = parent
    __lowerCamelCase       :       Optional[int]           = batch_size
    __lowerCamelCase       :       Union[str, Any]           = image_size
    __lowerCamelCase       :       Optional[int]           = num_channels
    __lowerCamelCase       :       Union[str, Any]           = embeddings_size
    __lowerCamelCase       :       Optional[Any]           = hidden_sizes
    __lowerCamelCase       :       Optional[int]           = depths
    __lowerCamelCase       :       int           = is_training
    __lowerCamelCase       :       Tuple           = use_labels
    __lowerCamelCase       :       Optional[Any]           = hidden_act
    __lowerCamelCase       :       List[Any]           = num_labels
    __lowerCamelCase       :       Tuple           = scope
    __lowerCamelCase       :       Dict           = len(_a   )




  def 						_lowercase     ( self      :	Any   )					->					List[str]:
    __lowerCamelCase       :       Optional[int]           = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

    return config, pixel_values




  def 						_lowercase     ( self      :	List[str]   )					->					Tuple:
    return RegNetConfig(
        num_channels=self.num_channels			,	embeddings_size=self.embeddings_size			,	hidden_sizes=self.hidden_sizes			,	depths=self.depths			,	hidden_act=self.hidden_act			,	num_labels=self.num_labels			,	image_size=self.image_size			,	)




  def 						_lowercase     ( self      :	List[str]			,	_a      :	Dict			,	_a      :	str   )					->					Optional[int]:
    __lowerCamelCase       :       List[str]           = FlaxRegNetModel(config=_a   )
    __lowerCamelCase       :       Any           = model(_a   )

    # Output shape (b, c, h, w)
    self.parent.assertEqual(
        result.last_hidden_state.shape			,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,	)




  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	List[Any]			,	_a      :	str   )					->					Tuple:
    __lowerCamelCase       :       Any           = self.num_labels
    __lowerCamelCase       :       Union[str, Any]           = FlaxRegNetForImageClassification(config=_a   )
    __lowerCamelCase       :       Optional[Any]           = model(_a   )
    self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_labels)   )




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






@require_flax
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

  a_												=False
  a_												=False
  a_												=False




  def 						_lowercase     ( self      :	int   )					->					None:
    __lowerCamelCase       :       Union[str, Any]           = FlaxRegNetModelTester(self   )
    __lowerCamelCase       :       Optional[Any]           = ConfigTester(self			,	config_class=_a			,	has_text_modality=_a   )




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Optional[int]:
    self.create_and_test_config_common_properties()
    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 						_lowercase     ( self      :	Optional[int]   )					->					Optional[Any]:
    return




  def 						_lowercase     ( self      :	List[Any]   )					->					Optional[Any]:
    __lowerCamelCase       :       Optional[int]           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_a   )




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					str:
    __lowerCamelCase       :       List[Any]           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*_a   )




  @unittest.skip(reason='RegNet does not use inputs_embeds'   )
  def 						_lowercase     ( self      :	Optional[int]   )					->					str:
    pass




  @unittest.skip(reason='RegNet does not support input and output embeddings'   )
  def 						_lowercase     ( self      :	Optional[int]   )					->					Any:
    pass




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Optional[Any]:
    __lowerCamelCase       ,__lowerCamelCase       :       str           = self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __lowerCamelCase       :       Tuple           = model_class(_a   )
      __lowerCamelCase       :       str           = inspect.signature(model.__call__   )
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      __lowerCamelCase       :       Dict           = [*signature.parameters.keys()]

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




  def 						_lowercase     ( self      :	Tuple   )					->					Dict:
    def check_hidden_states_output(_a      :	Any			,	_a      :	int			,	_a      :	Optional[Any]   ):
      __lowerCamelCase       :       Optional[Any]           = model_class(_a   )

      __lowerCamelCase       :       Dict           = model(**self._prepare_for_class(_a			,	_a   )   )

      __lowerCamelCase       :       Optional[Any]           = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

      __lowerCamelCase       :       Any           = self.model_tester.num_stages
      self.assertEqual(len(_a   )			,	expected_num_stages + 1   )

    __lowerCamelCase       ,__lowerCamelCase       :       Tuple           = self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __lowerCamelCase       :       str           = True
      check_hidden_states_output(_a			,	_a			,	_a   )

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

      check_hidden_states_output(_a			,	_a			,	_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Optional[int]:
    __lowerCamelCase       ,__lowerCamelCase       :       Optional[int]           = self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__   ):
        __lowerCamelCase       :       Union[str, Any]           = self._prepare_for_class(_a			,	_a   )
        __lowerCamelCase       :       Tuple           = model_class(_a   )

        @jax.jit
        def model_jitted(_a      :	str			,	**_a      :	str   ):
          return model(pixel_values=_a			,	**_a   )

        with self.subTest('JIT Enabled'   ):
          __lowerCamelCase       :       Union[str, Any]           = model_jitted(**_a   ).to_tuple()

        with self.subTest('JIT Disabled'   ):
          with jax.disable_jit():
            __lowerCamelCase       :       List[Any]           = model_jitted(**_a   ).to_tuple()

        self.assertEqual(len(_a   )			,	len(_a   )   )
        for jitted_output, output in zip(_a			,	_a   ):
          self.assertEqual(jitted_output.shape			,	output.shape   )




def        a_     (							)      ->      Union[str, Any]:
  __lowerCamelCase       :       List[Any]           = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
  return image






@require_flax
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  @cached_property
  def 						_lowercase     ( self      :	Tuple   )					->					List[Any]:
    return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'   ) if is_vision_available() else None




  @slow
  def 						_lowercase     ( self      :	Tuple   )					->					Optional[Any]:
    __lowerCamelCase       :       Union[str, Any]           = FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'   )

    __lowerCamelCase       :       int           = self.default_image_processor
    __lowerCamelCase       :       List[str]           = prepare_img()
    __lowerCamelCase       :       Optional[Any]           = image_processor(images=_a			,	return_tensors='np'   )

    __lowerCamelCase       :       str           = model(**_a   )

    # verify the logits
    __lowerCamelCase       :       Any           = (1, 1000)
    self.assertEqual(outputs.logits.shape			,	_a   )

    __lowerCamelCase       :       Union[str, Any]           = jnp.array([-0.4180, -1.5051, -3.4836]   )

    self.assertTrue(jnp.allclose(outputs.logits[0, :3]			,	_a			,	atol=1e-4   )   )











'''simple docstring'''





import argparse
from pathlib import Path

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

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,)      ->      Optional[int]:
  if config_name_or_path is None:
    __lowerCamelCase       :       List[Any]           = 'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

  if generator_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Optional[int]           = generator_name_or_path

  if question_encoder_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Tuple           = question_encoder_name_or_path

  __lowerCamelCase       :       Union[str, Any]           = RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

  # Save model.
  __lowerCamelCase       :       Tuple           = RagConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       List[Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )

  __lowerCamelCase       :       Tuple           = gen_config
  __lowerCamelCase       :       List[Any]           = question_encoder_config

  __lowerCamelCase       :       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.
  __lowerCamelCase       :       List[str]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'       )
  __lowerCamelCase       :       Optional[int]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'       )


if __name__ == "__main__":
   _UpperCamelCase											=						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``'
       ),
   )

   _UpperCamelCase											=						parser.parse_args()

   _UpperCamelCase											=						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,
   )






