
"""simple docstring"""





from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from ..image_utils import load_image


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

_a														=		logging.get_logger(__name__)


_a														=		Dict[str, Any]
_a														=		List[Prediction]






@add_end_docstrings(lowerCamelCase	)
class     _UpperCAmelCase(    lowerCamelCase	):



		def __init__(					self				,		*__a				,		**__a)       ->       List[str]:






							'''simple docstring'''
							super().__init__(*__a				,		**__a)

							if self.framework == "tf":
												raise ValueError(F'''The {self.__class__} is only available in PyTorch.''')

							requires_backends(self				,		'''vision''')
							self.check_model_type(
							    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))



		def 						UpperCAmelCase							(					self				,		**__a)       ->       Tuple:






							'''simple docstring'''
							_UpperCamelCase							 =  {}
							if "threshold" in kwargs:
												_UpperCamelCase							 =  kwargs['''threshold''']
							return {}, {}, postprocess_kwargs



		def __call__(					self				,		*__a				,		**__a)       ->       Union[Predictions, List[Prediction]]:






							'''simple docstring'''
							return super().__call__(*__a				,		**__a)



		def 						UpperCAmelCase							(					self				,		__a)       ->       Optional[int]:






							'''simple docstring'''
							_UpperCamelCase							 =  load_image(__a)
							_UpperCamelCase							 =  torch.IntTensor([[image.height, image.width]])
							_UpperCamelCase							 =  self.image_processor(images=[image]				,		return_tensors='''pt''')
							if self.tokenizer is not None:
												_UpperCamelCase							 =  self.tokenizer(text=inputs['''words''']				,		boxes=inputs['''boxes''']				,		return_tensors='''pt''')
							_UpperCamelCase							 =  target_size
							return inputs



		def 						UpperCAmelCase							(					self				,		__a)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  model_inputs.pop('''target_size''')
							_UpperCamelCase							 =  self.model(**__a)
							_UpperCamelCase							 =  outputs.__class__({'''target_size''': target_size, **outputs})
							if self.tokenizer is not None:
												_UpperCamelCase							 =  model_inputs['''bbox''']
							return model_outputs



		def 						UpperCAmelCase							(					self				,		__a				,		__a=0.9)       ->       Tuple:






							'''simple docstring'''
							_UpperCamelCase							 =  model_outputs['''target_size''']
							if self.tokenizer is not None:
												# This is a LayoutLMForTokenClassification variant.
												# The OCR got the boxes and the model classified the words.
												_UpperCamelCase ,  _UpperCamelCase							 =  target_size[0].tolist()

												def unnormalize(__a):
																	return self._get_bounding_box(
																	    torch.Tensor(
																	        [
																	            (width * bbox[0] / 10_00),
																	            (height * bbox[1] / 10_00),
																	            (width * bbox[2] / 10_00),
																	            (height * bbox[3] / 10_00),
																	        ]))

												_UpperCamelCase ,  _UpperCamelCase							 =  model_outputs['''logits'''].squeeze(0).softmax(dim=-1).max(dim=-1)
												_UpperCamelCase							 =  [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
												_UpperCamelCase							 =  [unnormalize(__a) for bbox in model_outputs['''bbox'''].squeeze(0)]
												_UpperCamelCase							 =  ['''score''', '''label''', '''box''']
												_UpperCamelCase							 =  [dict(zip(__a				,		__a)) for vals in zip(scores.tolist()				,		__a				,		__a) if vals[0] > threshold]
							else:
												# This is a regular ForObjectDetectionModel
												_UpperCamelCase							 =  self.image_processor.post_process_object_detection(__a				,		__a				,		__a)
												_UpperCamelCase							 =  raw_annotations[0]
												_UpperCamelCase							 =  raw_annotation['''scores''']
												_UpperCamelCase							 =  raw_annotation['''labels''']
												_UpperCamelCase							 =  raw_annotation['''boxes''']

												_UpperCamelCase							 =  scores.tolist()
												_UpperCamelCase							 =  [self.model.config.idalabel[label.item()] for label in labels]
												_UpperCamelCase							 =  [self._get_bounding_box(__a) for box in boxes]

												# {"scores": [...], ...} --> [{"score":x, ...}, ...]
												_UpperCamelCase							 =  ['''score''', '''label''', '''box''']
												_UpperCamelCase							 =  [
												    dict(zip(__a				,		__a))
												    for vals in zip(raw_annotation['''scores''']				,		raw_annotation['''labels''']				,		raw_annotation['''boxes'''])
												]

							return annotation




		def 						UpperCAmelCase							(					self				,		__a)       ->       Dict[str, int]:






							'''simple docstring'''
							if self.framework != "pt":
												raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.''')
							_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  box.int().tolist()
							_UpperCamelCase							 =  {
							    '''xmin''': xmin,
							    '''ymin''': ymin,
							    '''xmax''': xmax,
							    '''ymax''': ymax,
							}
							return bbox








"""simple docstring"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_a														=		argparse.ArgumentParser(
				    description=(
				        """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
				        """ Distillation"""
				    )
				)
				parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
				parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
				parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
				parser.add_argument("""--vocab_transform""", action="""store_true""")
				_a														=		parser.parse_args()

				if args.model_type == "bert":
								_a														=		BertForMaskedLM.from_pretrained(args.model_name)
								_a														=		"""bert"""
				else:
								raise ValueError("""args.model_type should be \"bert\".""")

				_a														=		model.state_dict()
				_a														=		{}

				for w in ["word_embeddings", "position_embeddings"]:
								_a														=		state_dict[F"""{prefix}.embeddings.{w}.weight"""]
				for w in ["weight", "bias"]:
								_a														=		state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

				_a														=		0
				for teacher_idx in [0, 2, 4, 7, 9, 11]:
								for w in ["weight", "bias"]:
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
												]

												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
												]

												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
												]
								std_idx += 1

				_a														=		state_dict["""cls.predictions.decoder.weight"""]
				_a														=		state_dict["""cls.predictions.bias"""]
				if args.vocab_transform:
								for w in ["weight", "bias"]:
												_a														=		state_dict[F"""cls.predictions.transform.dense.{w}"""]
												_a														=		state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

				print(F"""N layers selected for distillation: {std_idx}""")
				print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

				print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
				torch.save(compressed_sd, args.dump_checkpoint)






