
from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
  import PIL

# soft dependency
if is_pytesseract_available():
  import pytesseract

lowercase  : Optional[int]						   =							logging.get_logger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[str]:
       return [
           int(1_000 * (box[0] / width)    ),
           int(1_000 * (box[1] / height)    ),
           int(1_000 * (box[2] / width)    ),
           int(1_000 * (box[3] / height)    ),
       ]







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = None    )			->   List[Any]:
       lowercase							:					Union[str, Any]	     =				tesseract_config if tesseract_config is not None else """"""

       # apply OCR
       lowercase							:					Optional[Any]	     =				to_pil_image(SCREAMING_SNAKE_CASE__    )
       lowercase     ,      lowercase							:					List[Any]	     =				pil_image.size
       lowercase							:					int	     =				pytesseract.image_to_data(SCREAMING_SNAKE_CASE__ ,     lang=SCREAMING_SNAKE_CASE__ ,     output_type="""dict""" ,     config=SCREAMING_SNAKE_CASE__    )
       lowercase     ,      lowercase     ,      lowercase     ,      lowercase     ,      lowercase							:					Union[str, Any]	     =				data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

       # filter empty words and corresponding coordinates
       lowercase							:					Optional[int]	     =				[idx for idx, word in enumerate(SCREAMING_SNAKE_CASE__    ) if not word.strip()]
       lowercase							:					Optional[int]	     =				[word for idx, word in enumerate(SCREAMING_SNAKE_CASE__    ) if idx not in irrelevant_indices]
       lowercase							:					Optional[Any]	     =				[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__    ) if idx not in irrelevant_indices]
       lowercase							:					Optional[int]	     =				[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__    ) if idx not in irrelevant_indices]
       lowercase							:					Dict	     =				[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__    ) if idx not in irrelevant_indices]
       lowercase							:					Union[str, Any]	     =				[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__    ) if idx not in irrelevant_indices]

       # turn coordinates into (left, top, left+width, top+height) format
       lowercase							:					Optional[int]	     =				[]
       for x, y, w, h in zip(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
              lowercase							:					List[str]	     =				[x, y, x + w, y + h]
              actual_boxes.append(SCREAMING_SNAKE_CASE__    )

       # finally, normalize the bounding boxes
       lowercase							:					Optional[int]	     =				[]
       for box in actual_boxes:
              normalized_boxes.append(normalize_box(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )    )

       assert len(SCREAMING_SNAKE_CASE__    ) == len(SCREAMING_SNAKE_CASE__    ), "Not as many words as there are bounding boxes"

       return words, normalized_boxes




class __snake_case		(				lowerCAmelCase					):
    _a		:						str=    ["pixel_values"]
    def __init__(  self		,snake_case = True		,snake_case = None		,snake_case = PILImageResampling.BILINEAR		,snake_case = True		,snake_case = None		,snake_case = ""		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case				)
           lowercase							:					Tuple	     =				size if size is not None else {"""height""": 224, """width""": 224}
           lowercase							:					Dict	     =				get_size_dict(snake_case				)

           lowercase							:					Dict	     =				do_resize
           lowercase							:					Optional[Any]	     =				size
           lowercase							:					Union[str, Any]	     =				resample
           lowercase							:					Tuple	     =				apply_ocr
           lowercase							:					str	     =				ocr_lang
           lowercase							:					Dict	     =				tesseract_config
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case = PILImageResampling.BILINEAR		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					List[str]	     =				get_size_dict(snake_case				)
           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()}"				)
           lowercase							:					List[str]	     =				(size["""height"""], size["""width"""])
           return resize(snake_case		,size=snake_case		,resample=snake_case		,data_format=snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = ChannelDimension.FIRST		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					int	     =				do_resize if do_resize is not None else self.do_resize
           lowercase							:					List[str]	     =				size if size is not None else self.size
           lowercase							:					int	     =				get_size_dict(snake_case				)
           lowercase							:					Union[str, Any]	     =				resample if resample is not None else self.resample
           lowercase							:					int	     =				apply_ocr if apply_ocr is not None else self.apply_ocr
           lowercase							:					Dict	     =				ocr_lang if ocr_lang is not None else self.ocr_lang
           lowercase							:					Optional[Any]	     =				tesseract_config if tesseract_config is not None else self.tesseract_config

           lowercase							:					Optional[Any]	     =				make_list_of_images(snake_case				)

           if not valid_images(snake_case				):
                  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."""				)

           # All transformations expect numpy arrays.
           lowercase							:					Optional[int]	     =				[to_numpy_array(snake_case				) for image in images]

           if apply_ocr:
                  requires_backends(self		,"""pytesseract"""				)
                  lowercase							:					Any	     =				[]
                  lowercase							:					Optional[Any]	     =				[]
                  for image in images:
                         lowercase     ,      lowercase							:					Optional[Any]	     =				apply_tesseract(snake_case		,snake_case		,snake_case				)
                         words_batch.append(snake_case				)
                         boxes_batch.append(snake_case				)

           if do_resize:
                  lowercase							:					List[str]	     =				[self.resize(image=snake_case		,size=snake_case		,resample=snake_case				) for image in images]

           # flip color channels from RGB to BGR (as Detectron2 requires this)
           lowercase							:					str	     =				[flip_channel_order(snake_case				) for image in images]
           lowercase							:					Union[str, Any]	     =				[to_channel_dimension_format(snake_case		,snake_case				) for image in images]

           lowercase							:					Union[str, Any]	     =				BatchFeature(data={"""pixel_values""": images}		,tensor_type=snake_case				)

           if apply_ocr:
                  lowercase							:					List[Any]	     =				words_batch
                  lowercase							:					Any	     =				boxes_batch
           return data


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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:
       # Load configuration defined in the metadata file
       with open(SCREAMING_SNAKE_CASE__    ) as metadata_file:
              lowercase							:					Union[str, Any]	     =				json.load(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Tuple	     =				LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__ ,     **metadata["""model_config"""]    )

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

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

       lowercase							:					Dict	     =				XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]    )

       # Add special tokens to the token vocabulary for downstream tasks
       lowercase							:					List[Any]	     =				AddedToken("""<ent>""" ,     lstrip=SCREAMING_SNAKE_CASE__ ,     rstrip=SCREAMING_SNAKE_CASE__    )
       lowercase							:					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:
              lowercase							:					List[str]	     =				json.load(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Optional[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__    )

       lowercase							:					int	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )

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

       lowercase							:					int	     =				state_dict["""embeddings.word_embeddings.weight"""]
       lowercase							:					Union[str, Any]	     =				word_emb[ent_init_index].unsqueeze(0    )
       lowercase							:					List[str]	     =				word_emb[enta_init_index].unsqueeze(0    )
       lowercase							:					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"]:
              lowercase							:					List[Any]	     =				state_dict[bias_name]
              lowercase							:					Any	     =				decoder_bias[ent_init_index].unsqueeze(0    )
              lowercase							:					Tuple	     =				decoder_bias[enta_init_index].unsqueeze(0    )
              lowercase							:					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"]:
                     lowercase							:					Union[str, Any]	     =				f"encoder.layer.{layer_index}.attention.self."
                     lowercase							:					List[str]	     =				state_dict[prefix + matrix_name]
                     lowercase							:					Any	     =				state_dict[prefix + matrix_name]
                     lowercase							:					str	     =				state_dict[prefix + matrix_name]

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

       lowercase							:					List[str]	     =				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"""    )
       lowercase							:					List[str]	     =				OrderedDict()
       for key, value in state_dict.items():
              if not (key.startswith("""lm_head"""    ) or key.startswith("""entity_predictions"""    )):
                     lowercase							:					List[Any]	     =				state_dict[key]
              else:
                     lowercase							:					Union[str, Any]	     =				state_dict[key]

       lowercase     ,      lowercase							:					int	     =				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
       lowercase							:					str	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ ,     task="""entity_classification"""    )

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

       lowercase							:					Any	     =				model(**SCREAMING_SNAKE_CASE__    )

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              lowercase							:					List[Any]	     =				torch.Size((1, 33, 768)    )
              lowercase							:					str	     =				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
              lowercase							:					Optional[int]	     =				torch.Size((1, 1, 768)    )
              lowercase							:					List[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
       lowercase							:					Any	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[Any]	     =				"""Tokyo is the capital of <mask>."""
       lowercase							:					List[Any]	     =				(24, 30)
       lowercase							:					int	     =				tokenizer(SCREAMING_SNAKE_CASE__ ,     entity_spans=[span] ,     return_tensors="""pt"""    )

       lowercase							:					Dict	     =				model(**SCREAMING_SNAKE_CASE__    )

       lowercase							:					Union[str, Any]	     =				encoding["""input_ids"""][0].tolist()
       lowercase							:					List[Any]	     =				input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""    )    )
       lowercase							:					Dict	     =				outputs.logits[0][mask_position_id].argmax(dim=-1    )
       assert "Japan" == tokenizer.decode(SCREAMING_SNAKE_CASE__    )

       lowercase							:					Union[str, Any]	     =				outputs.entity_logits[0][0].argmax().item()
       lowercase							:					int	     =				[
           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 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Dict:
       lowercase							:					Optional[int]	     =				["""[MASK]""", """[PAD]""", """[UNK]"""]

       lowercase							:					List[str]	     =				[json.loads(SCREAMING_SNAKE_CASE__    ) for line in open(SCREAMING_SNAKE_CASE__    )]

       lowercase							:					int	     =				{}
       for entry in data:
              lowercase							:					Optional[Any]	     =				entry["""id"""]
              for entity_name, language in entry["entities"]:
                     if entity_name in SPECIAL_TOKENS:
                            lowercase							:					Optional[Any]	     =				entity_id
                            break
                     lowercase							:					List[Any]	     =				f"{language}:{entity_name}"
                     lowercase							:					Union[str, Any]	     =				entity_id
       return new_mapping


if __name__ == "__main__":
  lowercase  : 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."""
  )
  lowercase  : str						   =							parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )
