

"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A						       =      logging.get_logger(__name__)

__A						       =      {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}





class    snake_case     (    __snake_case,							__snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  """swin"""

				SCREAMING_SNAKE_CASE_       :				int       				=  {
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}

				def __init__(     self							:    int					,  UpperCamelCase__							:    str=2_2_4					,  UpperCamelCase__							:    Dict=4					,  UpperCamelCase__							:    Union[str, Any]=3					,  UpperCamelCase__							:    List[str]=9_6					,  UpperCamelCase__							:    int=[2, 2, 6, 2]					,  UpperCamelCase__							:    Union[str, Any]=[3, 6, 1_2, 2_4]					,  UpperCamelCase__							:    List[str]=7					,  UpperCamelCase__							:    Optional[Any]=4.0					,  UpperCamelCase__							:    Optional[Any]=True					,  UpperCamelCase__							:    Union[str, Any]=0.0					,  UpperCamelCase__							:    str=0.0					,  UpperCamelCase__							:    Any=0.1					,  UpperCamelCase__							:    Dict="gelu"					,  UpperCamelCase__							:    Any=False					,  UpperCamelCase__							:    Any=0.02					,  UpperCamelCase__							:    Tuple=1e-5					,  UpperCamelCase__							:    Tuple=3_2					,  UpperCamelCase__							:    Tuple=None					,  UpperCamelCase__							:    str=None					,  **UpperCamelCase__							:    List[str]					,  )->       Union[str, Any]:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						__lowerCAmelCase:	Any   				=      image_size
						__lowerCAmelCase:	Union[str, Any]   				=      patch_size
						__lowerCAmelCase:	Tuple   				=      num_channels
						__lowerCAmelCase:	Any   				=      embed_dim
						__lowerCAmelCase:	Optional[Any]   				=      depths
						__lowerCAmelCase:	List[str]   				=      len(UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      num_heads
						__lowerCAmelCase:	int   				=      window_size
						__lowerCAmelCase:	Dict   				=      mlp_ratio
						__lowerCAmelCase:	List[Any]   				=      qkv_bias
						__lowerCAmelCase:	Optional[int]   				=      hidden_dropout_prob
						__lowerCAmelCase:	Any   				=      attention_probs_dropout_prob
						__lowerCAmelCase:	str   				=      drop_path_rate
						__lowerCAmelCase:	List[Any]   				=      hidden_act
						__lowerCAmelCase:	Optional[Any]   				=      use_absolute_embeddings
						__lowerCAmelCase:	Union[str, Any]   				=      layer_norm_eps
						__lowerCAmelCase:	List[Any]   				=      initializer_range
						__lowerCAmelCase:	Union[str, Any]   				=      encoder_stride
						# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
						# this indicates the channel dimension after the last stage of the model
						__lowerCAmelCase:	Dict   				=      int(embed_dim * 2 ** (len(UpperCamelCase__) - 1))
						__lowerCAmelCase:	List[str]   				=      ["stem"] + [f"stage{idx}" for idx in range(1					,  len(UpperCamelCase__) + 1)]
						__lowerCAmelCase				,			__lowerCAmelCase:	Tuple   				=      get_aligned_output_features_output_indices(
						    out_features=UpperCamelCase__					,  out_indices=UpperCamelCase__					,  stage_names=self.stage_names)





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  version.parse("""1.11"""  )

				@property
				def 							lowercase_					(     self							:    str)->       Mapping[str, Mapping[int, str]]:



						'''simple docstring'''


						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						    ])




				@property
				def 							lowercase_					(     self							:    List[str])->       float:



						'''simple docstring'''


						return 1e-4





"""simple docstring"""


import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken






@torch.no_grad()
def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Any:
		# Load configuration defined in the metadata file
		with open(__SCREAMING_SNAKE_CASE			) as metadata_file:
				__lowerCAmelCase:	List[Any]   				=      json.load(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Dict   				=      LukeConfig(use_entity_aware_attention=__SCREAMING_SNAKE_CASE ,			**metadata["model_config"]			)

		# Load in the weights from the checkpoint_path
		__lowerCAmelCase:	Optional[Any]   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location="cpu"			)["module"]

		# Load the entity vocab file
		__lowerCAmelCase:	List[Any]   				=      load_original_entity_vocab(__SCREAMING_SNAKE_CASE			)
		# add an entry for [MASK2]
		__lowerCAmelCase:	Any   				=      max(entity_vocab.values()			) + 1
		config.entity_vocab_size += 1

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

		# Add special tokens to the token vocabulary for downstream tasks
		__lowerCAmelCase:	str   				=      AddedToken("<ent>" ,			lstrip=__SCREAMING_SNAKE_CASE ,			rstrip=__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Optional[int]   				=      AddedToken("<ent2>" ,			lstrip=__SCREAMING_SNAKE_CASE ,			rstrip=__SCREAMING_SNAKE_CASE			)
		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(__SCREAMING_SNAKE_CASE			)
		with open(os.path.join(__SCREAMING_SNAKE_CASE ,			"tokenizer_config.json"			) ,			"r"			) as f:
				__lowerCAmelCase:	Optional[int]   				=      json.load(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Any   				=      "MLukeTokenizer"
		with open(os.path.join(__SCREAMING_SNAKE_CASE ,			"tokenizer_config.json"			) ,			"w"			) as f:
				json.dump(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

		with open(os.path.join(__SCREAMING_SNAKE_CASE ,			MLukeTokenizer.vocab_files_names["entity_vocab_file"]			) ,			"w"			) as f:
				json.dump(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)

		# Initialize the embeddings of the special tokens
		__lowerCAmelCase:	Union[str, Any]   				=      tokenizer.convert_tokens_to_ids(["@"]			)[0]
		__lowerCAmelCase:	Optional[int]   				=      tokenizer.convert_tokens_to_ids(["#"]			)[0]

		__lowerCAmelCase:	Dict   				=      state_dict["embeddings.word_embeddings.weight"]
		__lowerCAmelCase:	Optional[int]   				=      word_emb[ent_init_index].unsqueeze(0			)
		__lowerCAmelCase:	int   				=      word_emb[enta_init_index].unsqueeze(0			)
		__lowerCAmelCase:	str   				=      torch.cat([word_emb, ent_emb, enta_emb]			)
		# add special tokens for 'entity_predictions.bias'
		for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
				__lowerCAmelCase:	Dict   				=      state_dict[bias_name]
				__lowerCAmelCase:	Union[str, Any]   				=      decoder_bias[ent_init_index].unsqueeze(0			)
				__lowerCAmelCase:	Optional[int]   				=      decoder_bias[enta_init_index].unsqueeze(0			)
				__lowerCAmelCase:	Optional[int]   				=      torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]			)

		# 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"]:
						__lowerCAmelCase:	Optional[int]   				=      F"encoder.layer.{layer_index}.attention.self."
						__lowerCAmelCase:	Tuple   				=      state_dict[prefix + matrix_name]
						__lowerCAmelCase:	Dict   				=      state_dict[prefix + matrix_name]
						__lowerCAmelCase:	Optional[Any]   				=      state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
		__lowerCAmelCase:	int   				=      state_dict["entity_embeddings.entity_embeddings.weight"]
		__lowerCAmelCase:	Dict   				=      entity_emb[entity_vocab["[MASK]"]].unsqueeze(0			)
		__lowerCAmelCase:	str   				=      torch.cat([entity_emb, entity_mask_emb]			)
		# add [MASK2] for 'entity_predictions.bias'
		__lowerCAmelCase:	List[str]   				=      state_dict["entity_predictions.bias"]
		__lowerCAmelCase:	Dict   				=      entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0			)
		__lowerCAmelCase:	str   				=      torch.cat([entity_prediction_bias, entity_mask_bias]			)

		__lowerCAmelCase:	Optional[int]   				=      LukeForMaskedLM(config=__SCREAMING_SNAKE_CASE			).eval()

		state_dict.pop("entity_predictions.decoder.weight"			)
		state_dict.pop("lm_head.decoder.weight"			)
		state_dict.pop("lm_head.decoder.bias"			)
		__lowerCAmelCase:	Tuple   				=      OrderedDict()
		for key, value in state_dict.items():
				if not (key.startswith("lm_head"			) or key.startswith("entity_predictions"			)):
						__lowerCAmelCase:	Any   				=      state_dict[key]
				else:
						__lowerCAmelCase:	Tuple   				=      state_dict[key]

		__lowerCAmelCase				,			__lowerCAmelCase:	Tuple   				=      model.load_state_dict(__SCREAMING_SNAKE_CASE ,			strict=__SCREAMING_SNAKE_CASE			)

		if set(__SCREAMING_SNAKE_CASE			) != {"luke.embeddings.position_ids"}:
				raise ValueError(F"Unexpected unexpected_keys: {unexpected_keys}"			)
		if set(__SCREAMING_SNAKE_CASE			) != {
		    "lm_head.decoder.weight",
		    "lm_head.decoder.bias",
		    "entity_predictions.decoder.weight",
		}:
				raise ValueError(F"Unexpected missing_keys: {missing_keys}"			)

		model.tie_weights()
		assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
		assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

		# Check outputs
		__lowerCAmelCase:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE ,			task="entity_classification"			)

		__lowerCAmelCase:	Tuple   				=      "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
		__lowerCAmelCase:	Optional[Any]   				=      (0, 9)
		__lowerCAmelCase:	Optional[int]   				=      tokenizer(__SCREAMING_SNAKE_CASE ,			entity_spans=[span] ,			return_tensors="pt"			)

		__lowerCAmelCase:	int   				=      model(**__SCREAMING_SNAKE_CASE			)

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				__lowerCAmelCase:	Dict   				=      torch.Size((1, 3_3, 7_6_8)			)
				__lowerCAmelCase:	Optional[int]   				=      torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]			)

		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] ,			__SCREAMING_SNAKE_CASE ,			atol=1E-4			):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				__lowerCAmelCase:	Union[str, Any]   				=      torch.Size((1, 1, 7_6_8)			)
				__lowerCAmelCase:	Any   				=      torch.tensor([[-0.1482, 0.0609, 0.0322]]			)

		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] ,			__SCREAMING_SNAKE_CASE ,			atol=1E-4			):
				raise ValueError

		# Verify masked word/entity prediction
		__lowerCAmelCase:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	List[Any]   				=      "Tokyo is the capital of <mask>."
		__lowerCAmelCase:	List[str]   				=      (2_4, 3_0)
		__lowerCAmelCase:	int   				=      tokenizer(__SCREAMING_SNAKE_CASE ,			entity_spans=[span] ,			return_tensors="pt"			)

		__lowerCAmelCase:	Union[str, Any]   				=      model(**__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Optional[int]   				=      encoding["input_ids"][0].tolist()
		__lowerCAmelCase:	int   				=      input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"			)			)
		__lowerCAmelCase:	Optional[Any]   				=      outputs.logits[0][mask_position_id].argmax(dim=-1			)
		assert "Japan" == tokenizer.decode(__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Any   				=      outputs.entity_logits[0][0].argmax().item()
		__lowerCAmelCase:	Union[str, Any]   				=      [
		    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
		]
		assert [e for e in multilingual_predicted_entities if e.startswith("en:"			)][0] == "en:Japan"

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






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Any:
		__lowerCAmelCase:	Tuple   				=      ["[MASK]", "[PAD]", "[UNK]"]

		__lowerCAmelCase:	Optional[Any]   				=      [json.loads(__SCREAMING_SNAKE_CASE			) for line in open(__SCREAMING_SNAKE_CASE			)]

		__lowerCAmelCase:	str   				=      {}
		for entry in data:
				__lowerCAmelCase:	Tuple   				=      entry["id"]
				for entity_name, language in entry["entities"]:
						if entity_name in SPECIAL_TOKENS:
								__lowerCAmelCase:	Optional[int]   				=      entity_id
								break
						__lowerCAmelCase:	Optional[Any]   				=      F"{language}:{entity_name}"
						__lowerCAmelCase:	Optional[int]   				=      entity_id
		return new_mapping


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,
			)


