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


UpperCamelCase  							=						logging.get_logger(__name__)



class   snake_case_       (		__A       ):
   __A	:		Any         =				["pixel_values"]






   def __init__(      self				:     str	,						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_				:     Dict	,						)					->						None:
       super().__init__(**lowercase_  )
       lowercase__ :   List[str]    					=				size if size is not None else {"shortest_edge": 2_24}
       lowercase__ :   List[Any]    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_  )
       lowercase__ :   Optional[Any]    					=				crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
       lowercase__ :   str    					=				get_size_dict(lowercase_	,						param_name="crop_size"  )

       lowercase__ :   Tuple    					=				do_resize
       lowercase__ :   int    					=				size
       lowercase__ :   str    					=				resample
       lowercase__ :   Optional[Any]    					=				do_rescale
       lowercase__ :   Tuple    					=				rescale_factor
       lowercase__ :   List[str]    					=				do_center_crop
       lowercase__ :   List[str]    					=				crop_size
       lowercase__ :   Dict    					=				do_flip_channel_order






   def       __UpperCamelCase     (      self				:     Optional[int]	,						lowercase_				:     np.ndarray	,						lowercase_				:     Dict[str, int]	,						lowercase_				:     PILImageResampling = PIL.Image.BILINEAR	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     str	,						)					->						np.ndarray:
       lowercase__ :   List[str]    					=				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()}'''  )
       lowercase__ :   Optional[int]    					=				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       __UpperCamelCase     (      self				:     str	,						lowercase_				:     np.ndarray	,						lowercase_				:     Dict[str, int]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     Any	,						)					->						np.ndarray:
       lowercase__ :   Any    					=				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       __UpperCamelCase     (      self				:     int	,						lowercase_				:     np.ndarray	,						lowercase_				:     Union[int, float]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     Dict	,						)					->						Optional[Any]:
       return rescale(lowercase_	,						scale=lowercase_	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     np.ndarray	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None  )					->						np.ndarray:
       return flip_channel_order(lowercase_	,						data_format=lowercase_  )






   def       __UpperCamelCase     (      self				:     Any	,						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_				:     Tuple	,						)					->						PIL.Image.Image:
       lowercase__ :   int    					=				do_resize if do_resize is not None else self.do_resize
       lowercase__ :   Any    					=				resample if resample is not None else self.resample
       lowercase__ :   Optional[Any]    					=				do_rescale if do_rescale is not None else self.do_rescale
       lowercase__ :   Union[str, Any]    					=				rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase__ :   int    					=				do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase__ :   int    					=				(
           do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
       )

       lowercase__ :   Dict    					=				size if size is not None else self.size
       lowercase__ :   int    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_  )
       lowercase__ :   str    					=				crop_size if crop_size is not None else self.crop_size
       lowercase__ :   str    					=				get_size_dict(lowercase_	,						param_name="crop_size"  )

       lowercase__ :   Optional[Any]    					=				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.
       lowercase__ :   Tuple    					=				[to_numpy_array(lowercase_  ) for image in images]

       if do_resize:
           lowercase__ :   int    					=				[self.resize(image=lowercase_	,						size=lowercase_	,						resample=lowercase_  ) for image in images]

       if do_center_crop:
           lowercase__ :   List[Any]    					=				[self.center_crop(image=lowercase_	,						size=lowercase_  ) for image in images]

       if do_rescale:
           lowercase__ :   Any    					=				[self.rescale(image=lowercase_	,						scale=lowercase_  ) for image in images]

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

       lowercase__ :   str    					=				[to_channel_dimension_format(lowercase_	,						lowercase_  ) for image in images]

       lowercase__ :   Optional[int]    					=				{"pixel_values": images}
       return BatchFeature(data=lowercase_	,						tensor_type=lowercase_  )







   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     int	,						lowercase_				:     List[Tuple] = None  )					->						List[Any]:
       lowercase__ :   Optional[int]    					=				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_  ):
               lowercase__ :   Any    					=				target_sizes.numpy()

           lowercase__ :   Optional[Any]    					=				[]

           for idx in range(len(lowercase_  )  ):
               lowercase__ :   Tuple    					=				torch.nn.functional.interpolate(
                   logits[idx].unsqueeze(dim=0  )	,						size=target_sizes[idx]	,						mode="bilinear"	,						align_corners=lowercase_  )
               lowercase__ :   Union[str, Any]    					=				resized_logits[0].argmax(dim=0  )
               semantic_segmentation.append(lowercase_  )
       else:
           lowercase__ :   Optional[int]    					=				logits.argmax(dim=1  )
           lowercase__ :   Optional[Any]    					=				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]  )]

       return semantic_segmentation





"""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)






