


import inspect
import unittest

from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaModel
 from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import MobileNetVaImageProcessor






class _lowerCamelCase(		_a  ):
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				List[str] 						=						self.config_class(**self.inputs_dict)
           self.parent.assertTrue(hasattr(lowerCamelCase, 'tf_padding'))
           self.parent.assertTrue(hasattr(lowerCamelCase, 'depth_multiplier'))






class _lowerCamelCase:
       def __init__(   self, lowerCamelCase, lowerCamelCase=13, lowerCamelCase=3, lowerCamelCase=32, lowerCamelCase=0.2_5, lowerCamelCase=8, lowerCamelCase=True, lowerCamelCase=10_24, lowerCamelCase=32, lowerCamelCase="relu6", lowerCamelCase=0.1, lowerCamelCase=0.0_2, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=10, lowerCamelCase=None, )			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						parent
           _lowercase			:				Any 						=						batch_size
           _lowercase			:				Dict 						=						num_channels
           _lowercase			:				Dict 						=						image_size
           _lowercase			:				List[str] 						=						depth_multiplier
           _lowercase			:				Tuple 						=						min_depth
           _lowercase			:				Union[str, Any] 						=						tf_padding
           _lowercase			:				Optional[int] 						=						int(last_hidden_size * depth_multiplier)
           _lowercase			:				Tuple 						=						output_stride
           _lowercase			:				Optional[Any] 						=						hidden_act
           _lowercase			:				Dict 						=						classifier_dropout_prob
           _lowercase			:				Optional[int] 						=						use_labels
           _lowercase			:				Optional[Any] 						=						is_training
           _lowercase			:				int 						=						num_labels
           _lowercase			:				Tuple 						=						initializer_range
           _lowercase			:				int 						=						scope
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _lowercase			:				str 						=						None
           _lowercase			:				Tuple 						=						None
           if self.use_labels:
               _lowercase			:				Optional[Any] 						=						ids_tensor([self.batch_size], self.num_labels)
               _lowercase			:				Tuple 						=						ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)

           _lowercase			:				Optional[Any] 						=						self.get_config()

           return config, pixel_values, labels, pixel_labels
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           return MobileNetVaConfig(
               num_channels=self.num_channels, image_size=self.image_size, depth_multiplier=self.depth_multiplier, min_depth=self.min_depth, tf_padding=self.tf_padding, hidden_act=self.hidden_act, classifier_dropout_prob=self.classifier_dropout_prob, initializer_range=self.initializer_range, )
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Dict 						=						MobileNetVaModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				List[Any] 						=						model(lowerCamelCase)
           self.parent.assertEqual(
               result.last_hidden_state.shape, (
                   self.batch_size,
                   self.last_hidden_size,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               ), )
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				List[str] 						=						self.num_labels
           _lowercase			:				str 						=						MobileNetVaForImageClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				List[Any] 						=						model(lowerCamelCase, labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))




       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				Dict 						=						self.prepare_config_and_inputs()
           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Tuple 						=						config_and_inputs
           _lowercase			:				Optional[Any] 						=						{'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(		_a,					_a,					unittest.TestCase  ):
       lowercase_					:      int       					=     (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
       lowercase_					:      Union[str, Any]       					=     (
           {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
           if is_torch_available()
           else {}
       )

       lowercase_					:      Dict       					=     False
       lowercase_					:      Optional[Any]       					=     False
       lowercase_					:      Any       					=     False
       lowercase_					:      List[str]       					=     False
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Any 						=						MobileNetVaModelTester(self)
           _lowercase			:				Optional[Any] 						=						MobileNetVaConfigTester(self, config_class=lowerCamelCase, has_text_modality=lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           self.config_tester.run_common_tests()
       @unittest.skip(reason='MobileNetV1 does not use inputs_embeds')
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           pass
       @unittest.skip(reason='MobileNetV1 does not support input and output embeddings')
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           pass
       @unittest.skip(reason='MobileNetV1 does not output attentions')
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           pass
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

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

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

               _lowercase			:				Any 						=						['pixel_values']
               self.assertListEqual(arg_names[:1], lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase			:				Tuple 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           def check_hidden_states_output(lowerCamelCase, lowerCamelCase, lowerCamelCase):
               _lowercase			:				Any 						=						model_class(lowerCamelCase)
               model.to(lowerCamelCase)
               model.eval()

               with torch.no_grad():
                   _lowercase			:				Any 						=						model(**self._prepare_for_class(lowerCamelCase, lowerCamelCase))

               _lowercase			:				List[Any] 						=						outputs.hidden_states

               _lowercase			:				Optional[Any] 						=						26
               self.assertEqual(len(lowerCamelCase), lowerCamelCase)

           _lowercase						,							_lowercase			:				Any 						=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase			:				Dict 						=						True
               check_hidden_states_output(lowerCamelCase, lowerCamelCase, lowerCamelCase)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _lowercase			:				Optional[Any] 						=						True

               check_hidden_states_output(lowerCamelCase, lowerCamelCase, lowerCamelCase)
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase)




       @slow
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase			:				str 						=						MobileNetVaModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)



def 							UpperCamelCase_( )       ->		Tuple:
    _lowercase			:				int 						=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
    return image






@require_torch
@require_vision
class _lowerCamelCase(		unittest.TestCase  ):
       @cached_property
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           return (
               MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v1_1.0_224') if is_vision_available() else None
           )




       @slow
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v1_1.0_224').to(lowerCamelCase)

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

           # forward pass
           with torch.no_grad():
               _lowercase			:				Tuple 						=						model(**lowerCamelCase)

           # verify the logits
           _lowercase			:				Tuple 						=						torch.Size((1, 10_01))
           self.assertEqual(outputs.logits.shape, lowerCamelCase)

           _lowercase			:				List[Any] 						=						torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]).to(lowerCamelCase)

           self.assertTrue(torch.allclose(outputs.logits[0, :3], lowerCamelCase, atol=1E-4))





"""simple docstring"""
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken




@torch.no_grad()
def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> Tuple:
      # Load configuration defined in the metadata file
      with open(lowerCAmelCase    ) as metadata_file:
            UpperCAmelCase__			:							Optional[int]							=    json.load(lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[int]							=    LukeConfig(use_entity_aware_attention=lowerCAmelCase			,	**metadata["""model_config"""]    )

      # Load in the weights from the checkpoint_path
      UpperCAmelCase__			:							Dict							=    torch.load(lowerCAmelCase			,	map_location="""cpu"""    )

      # Load the entity vocab file
      UpperCAmelCase__			:							List[str]							=    load_entity_vocab(lowerCAmelCase    )

      UpperCAmelCase__			:							Union[str, Any]							=    RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]    )

      # Add special tokens to the token vocabulary for downstream tasks
      UpperCAmelCase__			:							Any							=    AddedToken("""<ent>"""			,	lstrip=lowerCAmelCase			,	rstrip=lowerCAmelCase    )
      UpperCAmelCase__			:							Tuple							=    AddedToken("""<ent2>"""			,	lstrip=lowerCAmelCase			,	rstrip=lowerCAmelCase    )
      tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}    )
      config.vocab_size += 2

      print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""    )
      tokenizer.save_pretrained(lowerCAmelCase    )
      with open(os.path.join(lowerCAmelCase			,	LukeTokenizer.vocab_files_names["""entity_vocab_file"""]    )			,	"""w"""    ) as f:
            json.dump(lowerCAmelCase			,	lowerCAmelCase    )

      UpperCAmelCase__			:							int							=    LukeTokenizer.from_pretrained(lowerCAmelCase    )

      # Initialize the embeddings of the special tokens
      UpperCAmelCase__			:							Any							=    state_dict["""embeddings.word_embeddings.weight"""]
      UpperCAmelCase__			:							Any							=    word_emb[tokenizer.convert_tokens_to_ids(["""@"""]    )[0]].unsqueeze(0    )
      UpperCAmelCase__			:							List[Any]							=    word_emb[tokenizer.convert_tokens_to_ids(["""#"""]    )[0]].unsqueeze(0    )
      UpperCAmelCase__			:							List[Any]							=    torch.cat([word_emb, ent_emb, enta_emb]    )

      # Initialize the query layers of the entity-aware self-attention mechanism
      for layer_index in range(config.num_hidden_layers    ):
            for matrix_name in ["query.weight", "query.bias"]:
                  UpperCAmelCase__			:							Any							=    F"""encoder.layer.{layer_index}.attention.self."""
                  UpperCAmelCase__			:							List[Any]							=    state_dict[prefix + matrix_name]
                  UpperCAmelCase__			:							Dict							=    state_dict[prefix + matrix_name]
                  UpperCAmelCase__			:							Optional[int]							=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      UpperCAmelCase__			:							List[str]							=    state_dict["""entity_embeddings.entity_embeddings.weight"""]
      UpperCAmelCase__			:							int							=    entity_emb[entity_vocab["""[MASK]"""]]

      UpperCAmelCase__			:							Tuple							=    LukeModel(config=lowerCAmelCase    ).eval()

      UpperCAmelCase__					,		UpperCAmelCase__			:							Optional[Any]							=    model.load_state_dict(lowerCAmelCase			,	strict=lowerCAmelCase    )
      if not (len(lowerCAmelCase    ) == 1 and missing_keys[0] == "embeddings.position_ids"):
            raise ValueError(F"""Missing keys {', '.join(lowerCAmelCase    )}. Expected only missing embeddings.position_ids"""    )
      if not (all(key.startswith("""entity_predictions"""    ) or key.startswith("""lm_head"""    ) for key in unexpected_keys    )):
            raise ValueError(
                """Unexpected keys"""
                F""" {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'    ) or key.startswith('lm_head'    ))]    )}"""    )

      # Check outputs
      UpperCAmelCase__			:							Optional[Any]							=    LukeTokenizer.from_pretrained(lowerCAmelCase			,	task="""entity_classification"""    )

      UpperCAmelCase__			:							Dict							=    (
          """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
          """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
      )
      UpperCAmelCase__			:							List[Any]							=    (39, 42)
      UpperCAmelCase__			:							str							=    tokenizer(lowerCAmelCase			,	entity_spans=[span]			,	add_prefix_space=lowerCAmelCase			,	return_tensors="""pt"""    )

      UpperCAmelCase__			:							List[str]							=    model(**lowerCAmelCase    )

      # Verify word hidden states
      if model_size == "large":
            UpperCAmelCase__			:							Optional[Any]							=    torch.Size((1, 42, 10_24)    )
            UpperCAmelCase__			:							Union[str, Any]							=    torch.tensor(
                [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]    )
      else:  # base
            UpperCAmelCase__			:							Optional[int]							=    torch.Size((1, 42, 7_68)    )
            UpperCAmelCase__			:							Optional[Any]							=    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]    )

      if not (outputs.last_hidden_state.shape == expected_shape):
            raise ValueError(
                F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""    )
      if not torch.allclose(outputs.last_hidden_state[0, :3, :3]			,	lowerCAmelCase			,	atol=1E-4    ):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            UpperCAmelCase__			:							Union[str, Any]							=    torch.Size((1, 1, 10_24)    )
            UpperCAmelCase__			:							str							=    torch.tensor([[0.0466, -0.0106, -0.0179]]    )
      else:  # base
            UpperCAmelCase__			:							Tuple							=    torch.Size((1, 1, 7_68)    )
            UpperCAmelCase__			:							List[str]							=    torch.tensor([[0.1457, 0.1044, 0.0174]]    )

      if not (outputs.entity_last_hidden_state.shape != expected_shape):
            raise ValueError(
                F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
                F""" {expected_shape}"""    )
      if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]			,	lowerCAmelCase			,	atol=1E-4    ):
            raise ValueError

      # Finally, save our PyTorch model and tokenizer
      print("""Saving PyTorch model to {}""".format(lowerCAmelCase    )    )
      model.save_pretrained(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase    )	-> Union[str, Any]:
      UpperCAmelCase__			:							List[str]							=    {}
      with open(lowerCAmelCase			,	"""r"""			,	encoding="""utf-8"""    ) as f:
            for index, line in enumerate(lowerCAmelCase    ):
                  UpperCAmelCase__					,		UpperCAmelCase__			:							Tuple							=    line.rstrip().split("""\t"""    )
                  UpperCAmelCase__			:							int							=    index

      return entity_vocab


if __name__ == "__main__":
    _A							=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
    parser.add_argument(
        """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
    )
    parser.add_argument(
        """--entity_vocab_path""",
        default=None,
        type=str,
        help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
    )
    parser.add_argument(
        """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
    )
    _A							=  parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )

