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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	List[Any]			,					__UpperCamelCase   :	Union[str, Any]			,					__UpperCamelCase   :	List[str]     )							-> Optional[Any]:







		"""simple docstring"""





		with open(__UpperCamelCase     ) as metadata_file:
				SCREAMING_SNAKE_CASE__      							=				json.load(__UpperCamelCase     )
		SCREAMING_SNAKE_CASE__      							=				LukeConfig(use_entity_aware_attention=__UpperCamelCase			,					**metadata["""model_config"""]     )

		# Load in the weights from the checkpoint_path
		SCREAMING_SNAKE_CASE__      							=				torch.load(__UpperCamelCase			,					map_location="""cpu"""     )["""module"""]

		# Load the entity vocab file
		SCREAMING_SNAKE_CASE__      							=				load_original_entity_vocab(__UpperCamelCase     )
		# add an entry for [MASK2]
		SCREAMING_SNAKE_CASE__      							=				max(entity_vocab.values()     ) + 1
		config.entity_vocab_size += 1

		SCREAMING_SNAKE_CASE__      							=				XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]     )

		# Add special tokens to the token vocabulary for downstream tasks
		SCREAMING_SNAKE_CASE__      							=				AddedToken("""<ent>"""			,					lstrip=__UpperCamelCase			,					rstrip=__UpperCamelCase     )
		SCREAMING_SNAKE_CASE__      							=				AddedToken("""<ent2>"""			,					lstrip=__UpperCamelCase			,					rstrip=__UpperCamelCase     )
		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(__UpperCamelCase     )
		with open(os.path.join(__UpperCamelCase			,					"""tokenizer_config.json"""     )			,					"""r"""     ) as f:
				SCREAMING_SNAKE_CASE__      							=				json.load(__UpperCamelCase     )
		SCREAMING_SNAKE_CASE__      							=				"""MLukeTokenizer"""
		with open(os.path.join(__UpperCamelCase			,					"""tokenizer_config.json"""     )			,					"""w"""     ) as f:
				json.dump(__UpperCamelCase			,					__UpperCamelCase     )

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

		SCREAMING_SNAKE_CASE__      							=				MLukeTokenizer.from_pretrained(__UpperCamelCase     )

		# Initialize the embeddings of the special tokens
		SCREAMING_SNAKE_CASE__      							=				tokenizer.convert_tokens_to_ids(["""@"""]     )[0]
		SCREAMING_SNAKE_CASE__      							=				tokenizer.convert_tokens_to_ids(["""#"""]     )[0]

		SCREAMING_SNAKE_CASE__      							=				state_dict["""embeddings.word_embeddings.weight"""]
		SCREAMING_SNAKE_CASE__      							=				word_emb[ent_init_index].unsqueeze(0     )
		SCREAMING_SNAKE_CASE__      							=				word_emb[enta_init_index].unsqueeze(0     )
		SCREAMING_SNAKE_CASE__      							=				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"]:
				SCREAMING_SNAKE_CASE__      							=				state_dict[bias_name]
				SCREAMING_SNAKE_CASE__      							=				decoder_bias[ent_init_index].unsqueeze(0     )
				SCREAMING_SNAKE_CASE__      							=				decoder_bias[enta_init_index].unsqueeze(0     )
				SCREAMING_SNAKE_CASE__      							=				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"]:
						SCREAMING_SNAKE_CASE__      							=				f"""encoder.layer.{layer_index}.attention.self."""
						SCREAMING_SNAKE_CASE__      							=				state_dict[prefix + matrix_name]
						SCREAMING_SNAKE_CASE__      							=				state_dict[prefix + matrix_name]
						SCREAMING_SNAKE_CASE__      							=				state_dict[prefix + matrix_name]

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

		SCREAMING_SNAKE_CASE__      							=				LukeForMaskedLM(config=__UpperCamelCase     ).eval()

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

		SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				model.load_state_dict(__UpperCamelCase			,					strict=__UpperCamelCase     )

		if set(__UpperCamelCase     ) != {"luke.embeddings.position_ids"}:
				raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""     )
		if set(__UpperCamelCase     ) != {
		    "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
		SCREAMING_SNAKE_CASE__      							=				MLukeTokenizer.from_pretrained(__UpperCamelCase			,					task="""entity_classification"""     )

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

		SCREAMING_SNAKE_CASE__      							=				model(**__UpperCamelCase     )

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				SCREAMING_SNAKE_CASE__      							=				torch.Size((1, 33, 7_68)     )
				SCREAMING_SNAKE_CASE__      							=				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]			,					__UpperCamelCase			,					atol=1E-4     ):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				SCREAMING_SNAKE_CASE__      							=				torch.Size((1, 1, 7_68)     )
				SCREAMING_SNAKE_CASE__      							=				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]			,					__UpperCamelCase			,					atol=1E-4     ):
				raise ValueError

		# Verify masked word/entity prediction
		SCREAMING_SNAKE_CASE__      							=				MLukeTokenizer.from_pretrained(__UpperCamelCase     )
		SCREAMING_SNAKE_CASE__      							=				"""Tokyo is the capital of <mask>."""
		SCREAMING_SNAKE_CASE__      							=				(24, 30)
		SCREAMING_SNAKE_CASE__      							=				tokenizer(__UpperCamelCase			,					entity_spans=[span]			,					return_tensors="""pt"""     )

		SCREAMING_SNAKE_CASE__      							=				model(**__UpperCamelCase     )

		SCREAMING_SNAKE_CASE__      							=				encoding["""input_ids"""][0].tolist()
		SCREAMING_SNAKE_CASE__      							=				input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""     )     )
		SCREAMING_SNAKE_CASE__      							=				outputs.logits[0][mask_position_id].argmax(dim=-1     )
		assert "Japan" == tokenizer.decode(__UpperCamelCase     )

		SCREAMING_SNAKE_CASE__      							=				outputs.entity_logits[0][0].argmax().item()
		SCREAMING_SNAKE_CASE__      							=				[
		    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(__UpperCamelCase     )     )
		model.save_pretrained(__UpperCamelCase     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]     )							-> str:







		"""simple docstring"""





		SCREAMING_SNAKE_CASE__      							=				["""[MASK]""", """[PAD]""", """[UNK]"""]

		SCREAMING_SNAKE_CASE__      							=				[json.loads(__UpperCamelCase     ) for line in open(__UpperCamelCase     )]

		SCREAMING_SNAKE_CASE__      							=				{}
		for entry in data:
				SCREAMING_SNAKE_CASE__      							=				entry["""id"""]
				for entity_name, language in entry["entities"]:
						if entity_name in SPECIAL_TOKENS:
								SCREAMING_SNAKE_CASE__      							=				entity_id
								break
						SCREAMING_SNAKE_CASE__      							=				f"""{language}:{entity_name}"""
						SCREAMING_SNAKE_CASE__      							=				entity_id
		return new_mapping


if __name__ == "__main__":
			__lowerCamelCase      :			Union[str, Any]							       =		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.'''
			)
			__lowerCamelCase      :			int							       =		parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
   import PIL

if is_torch_available():
   import torch


__lowerCamelCase      :			int							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(   self      :    int	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = PILImageResampling.BILINEAR	,     _lowercase      :    bool = True	,     _lowercase      :    Union[int, float] = 1 / 2_55	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = True	,     **_lowercase      :    List[Any]	,     ):



     """simple docstring"""






     super().__init__(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				size if size is not None else {"""shortest_edge""": 2_24}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else {"""height""": 2_56, """width""": 2_56}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     param_name="""crop_size"""				)

     SCREAMING_SNAKE_CASE__      							=				do_resize
     SCREAMING_SNAKE_CASE__      							=				size
     SCREAMING_SNAKE_CASE__      							=				resample
     SCREAMING_SNAKE_CASE__      							=				do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_center_crop
     SCREAMING_SNAKE_CASE__      							=				crop_size
     SCREAMING_SNAKE_CASE__      							=				do_flip_channel_order




   def      __a						(   self      :    List[Any]	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    PILImageResampling = PIL.Image.BILINEAR	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    str	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     if "shortest_edge" not in size:
       raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""				)
     SCREAMING_SNAKE_CASE__      							=				get_resize_output_image_size(_lowercase	,     size=size["""shortest_edge"""]	,     default_to_square=_lowercase				)
     return resize(_lowercase	,     size=_lowercase	,     resample=_lowercase	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    str	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Any	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase				)
     if "height" not in size or "width" not in size:
       raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""				)
     return center_crop(_lowercase	,     size=(size["""height"""], size["""width"""])	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    np.ndarray	,     _lowercase      :    Union[int, float]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Any	,     ):



     """simple docstring"""






     return rescale(_lowercase	,     scale=_lowercase	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Tuple	,     _lowercase      :    np.ndarray	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None				):



     """simple docstring"""






     return flip_channel_order(_lowercase	,     data_format=_lowercase				)




   def      __a						(   self      :    List[str]	,     _lowercase      :    ImageInput	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = None	,     _lowercase      :    bool = None	,     _lowercase      :    float = None	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = None	,     _lowercase      :    Optional[Union[str, TensorType]] = None	,     _lowercase      :    ChannelDimension = ChannelDimension.FIRST	,     **_lowercase      :    int	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__      							=				resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__      							=				do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__      							=				(
         do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
     )

     SCREAMING_SNAKE_CASE__      							=				size if size is not None else self.size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     param_name="""crop_size"""				)

     SCREAMING_SNAKE_CASE__      							=				make_list_of_images(_lowercase				)

     if not valid_images(_lowercase				):
       raise ValueError(
           """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
           """torch.Tensor, tf.Tensor or jax.ndarray."""				)

     if do_resize and size is None:
       raise ValueError("""Size must be specified if do_resize is True."""				)

     if do_rescale and rescale_factor is None:
       raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

     if do_center_crop and crop_size is None:
       raise ValueError("""Crop size must be specified if do_center_crop is True."""				)

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__      							=				[to_numpy_array(_lowercase				) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__      							=				[self.resize(image=_lowercase	,     size=_lowercase	,     resample=_lowercase				) for image in images]

     if do_center_crop:
       SCREAMING_SNAKE_CASE__      							=				[self.center_crop(image=_lowercase	,     size=_lowercase				) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE__      							=				[self.rescale(image=_lowercase	,     scale=_lowercase				) for image in images]

     # the pretrained checkpoints assume images are BGR, not RGB
     if do_flip_channel_order:
       SCREAMING_SNAKE_CASE__      							=				[self.flip_channel_order(image=_lowercase				) for image in images]

     SCREAMING_SNAKE_CASE__      							=				[to_channel_dimension_format(_lowercase	,     _lowercase				) for image in images]

     SCREAMING_SNAKE_CASE__      							=				{"""pixel_values""": images}
     return BatchFeature(data=_lowercase	,     tensor_type=_lowercase				)




   def      __a						(   self      :    List[Any]	,     _lowercase      :    Dict	,     _lowercase      :    List[Tuple] = None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
       if len(_lowercase				) != len(_lowercase				):
         raise ValueError(
             """Make sure that you pass in as many target sizes as the batch dimension of the logits"""				)

       if is_torch_tensor(_lowercase				):
         SCREAMING_SNAKE_CASE__      							=				target_sizes.numpy()

       SCREAMING_SNAKE_CASE__      							=				[]

       for idx in range(len(_lowercase				)				):
         SCREAMING_SNAKE_CASE__      							=				torch.nn.functional.interpolate(
             logits[idx].unsqueeze(dim=0				)	,     size=target_sizes[idx]	,     mode="""bilinear"""	,     align_corners=_lowercase				)
         SCREAMING_SNAKE_CASE__      							=				resized_logits[0].argmax(dim=0				)
         semantic_segmentation.append(_lowercase				)
     else:
       SCREAMING_SNAKE_CASE__      							=				logits.argmax(dim=1				)
       SCREAMING_SNAKE_CASE__      							=				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

     return semantic_segmentation
