





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 							__SCREAMING_SNAKE_CASE       :






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=13	,			__lowerCAmelCase=3	,			__lowerCAmelCase=True	,			__lowerCAmelCase=True	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=224	,			__lowerCAmelCase=1000	,			__lowerCAmelCase=[3, 3, 6, 4]	,			__lowerCAmelCase=[48, 56, 112, 220]	,			):
														UpperCamelCase__					  =      parent
														UpperCamelCase__					  =      batch_size
														UpperCamelCase__					  =      num_channels
														UpperCamelCase__					  =      is_training
														UpperCamelCase__					  =      use_labels
														UpperCamelCase__					  =      hidden_dropout_prob
														UpperCamelCase__					  =      attention_probs_dropout_prob
														UpperCamelCase__					  =      num_labels
														UpperCamelCase__					  =      image_size
														UpperCamelCase__					  =      layer_depths
														UpperCamelCase__					  =      embed_dims






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

														UpperCamelCase__					  =      None
														if self.use_labels:
																					UpperCamelCase__					  =      ids_tensor([self.batch_size]	,			self.num_labels	)

														UpperCamelCase__					  =      self.get_config()

														return config, pixel_values, labels






							def 					_lowerCamelCase		(    self	):
														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=_lowerCamelCase	,			layer_scale_init_value=1E-5	,			)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      SwiftFormerModel(config=_lowerCamelCase	)
														model.to(_lowerCamelCase	)
														model.eval()
														UpperCamelCase__					  =      model(_lowerCamelCase	)
														self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, self.embed_dims[-1], 7, 7)	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      self.num_labels
														UpperCamelCase__					  =      SwiftFormerForImageClassification(_lowerCamelCase	)
														model.to(_lowerCamelCase	)
														model.eval()
														UpperCamelCase__					  =      model(_lowerCamelCase	,			labels=_lowerCamelCase	)
														self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_labels)	)

														UpperCamelCase__					  =      SwiftFormerForImageClassification(_lowerCamelCase	)
														model.to(_lowerCamelCase	)
														model.eval()

														UpperCamelCase__					  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
														UpperCamelCase__					  =      model(_lowerCamelCase	)
														self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_labels)	)







							def 					_lowerCamelCase		(    self	):
														(UpperCamelCase__)					  =      self.prepare_config_and_inputs()
														UpperCamelCase__					  =      {"""pixel_values""": pixel_values}
														return config, inputs_dict






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

							snake_case  :   Union[str, Any]      =							False
							snake_case  :   str      =							False
							snake_case  :   Tuple      =							False
							snake_case  :   Optional[int]      =							False
							snake_case  :   List[Any]      =							False






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      SwiftFormerModelTester(self	)
														UpperCamelCase__					  =      ConfigTester(
														    self	,			config_class=_lowerCamelCase	,			has_text_modality=_lowerCamelCase	,			hidden_size=37	,			num_attention_heads=12	,			num_hidden_layers=12	,			)






							def 					_lowerCamelCase		(    self	):
														self.config_tester.run_common_tests()






							@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""	)
							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					UpperCamelCase__					  =      model_class(_lowerCamelCase	)
																					UpperCamelCase__					  =      model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(_lowerCamelCase	,			nn.Linear	)	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_common()

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

																					UpperCamelCase__					  =      ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]	,			_lowerCamelCase	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*_lowerCamelCase	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase	)






							@slow
							def 					_lowerCamelCase		(    self	):

														for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					UpperCamelCase__					  =      SwiftFormerModel.from_pretrained(_lowerCamelCase	)
																					self.assertIsNotNone(_lowerCamelCase	)






							@unittest.skip(reason="""SwiftFormer does not output attentions"""	)
							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):

														def check_hidden_states_output(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
																					UpperCamelCase__					  =      model_class(_lowerCamelCase	)
																					model.to(_lowerCamelCase	)
																					model.eval()

																					with torch.no_grad():
																												UpperCamelCase__					  =      model(**self._prepare_for_class(_lowerCamelCase	,			_lowerCamelCase	)	)

																					UpperCamelCase__					  =      outputs.hidden_states

																					UpperCamelCase__					  =      8
																					self.assertEqual(len(_lowerCamelCase	)	,			_lowerCamelCase	)  # 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(_lowerCamelCase	)	):
																												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__					  =      self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					UpperCamelCase__					  =      True
																					check_hidden_states_output(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase	)

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					UpperCamelCase__					  =      True

																					check_hidden_states_output(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase	)






							def 					_lowerCamelCase		(    self	):

														def _config_zero_init(__lowerCAmelCase	):
																					UpperCamelCase__					  =      copy.deepcopy(_lowerCamelCase	)
																					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(_lowerCamelCase	,			_lowerCamelCase	,			1E-10	)
																												if isinstance(getattr(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase	)	,			_lowerCamelCase	):
																																			UpperCamelCase__					  =      _config_zero_init(getattr(_lowerCamelCase	,			_lowerCamelCase	)	)
																																			setattr(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase	)
																					return configs_no_init

														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_common()

														UpperCamelCase__					  =      _config_zero_init(_lowerCamelCase	)
														for model_class in self.all_model_classes:
																					UpperCamelCase__					  =      model_class(config=_lowerCamelCase	)
																					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 					_lowerCamelCase		(    self	):
														pass






def      _UpperCamelCase  ():






							"""simple docstring"""





							UpperCamelCase__					  =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
							return image

@require_torch
@require_vision
class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):






							@cached_property
							def 					_lowerCamelCase		(    self	):
														return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""	) if is_vision_available() else None







							@slow
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""	).to(_lowerCamelCase	)

														UpperCamelCase__					  =      self.default_image_processor
														UpperCamelCase__					  =      prepare_img()
														UpperCamelCase__					  =      image_processor(images=_lowerCamelCase	,			return_tensors="""pt"""	).to(_lowerCamelCase	)

														# forward pass
														with torch.no_grad():
																					UpperCamelCase__					  =      model(**_lowerCamelCase	)

														# verify the logits
														UpperCamelCase__					  =      torch.Size((1, 1000)	)
														self.assertEqual(outputs.logits.shape	,			_lowerCamelCase	)

														UpperCamelCase__					  =      torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]	).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,
    )

