




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


snake_case_ : List[str]    		=       logging.get_logger(__name__)







class __snake_case			(			a		):
   UpperCAmelCase__  :    List[Any]          =  ['''pixel_values''']







   def __init__(      self		:						Tuple					,						_snake_case		:						bool = True					,						_snake_case		:						Dict[str, int] = None					,						_snake_case		:						PILImageResampling = PILImageResampling.BILINEAR					,						_snake_case		:						bool = True					,						_snake_case		:						Union[int, float] = 1 / 255					,						_snake_case		:						bool = True					,						_snake_case		:						Dict[str, int] = None					,						_snake_case		:						bool = True					,						**_snake_case		:						int					,						):

        """simple docstring"""


        super().__init__(**_snake_case)
        UpperCAmelCase_          =							size if size is not None else {'''shortest_edge''': 224}
        UpperCAmelCase_          =							get_size_dict(_snake_case					,						default_to_square=_snake_case)
        UpperCAmelCase_          =							crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
        UpperCAmelCase_          =							get_size_dict(_snake_case					,						param_name='''crop_size''')

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







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

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						List[str]					,						_snake_case		:						np.ndarray					,						_snake_case		:						Dict[str, int]					,						_snake_case		:						Optional[Union[str, ChannelDimension]] = None					,						**_snake_case		:						Tuple					,						):

        """simple docstring"""


        UpperCAmelCase_          =							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()}""")
        return center_crop(_snake_case					,						size=(size['''height'''], size['''width'''])					,						data_format=_snake_case					,						**_snake_case)







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

        """simple docstring"""


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







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

        """simple docstring"""


        return flip_channel_order(_snake_case					,						data_format=_snake_case)







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

        """simple docstring"""


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

        UpperCAmelCase_          =							size if size is not None else self.size
        UpperCAmelCase_          =							get_size_dict(_snake_case					,						default_to_square=_snake_case)
        UpperCAmelCase_          =							crop_size if crop_size is not None else self.crop_size
        UpperCAmelCase_          =							get_size_dict(_snake_case					,						param_name='''crop_size''')

        UpperCAmelCase_          =							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.''')

        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.
        UpperCAmelCase_          =							[to_numpy_array(_snake_case) for image in images]

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

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

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

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

        UpperCAmelCase_          =							[to_channel_dimension_format(_snake_case					,						_snake_case) for image in images]

        UpperCAmelCase_          =							{'''pixel_values''': images}
        return BatchFeature(data=_snake_case					,						tensor_type=_snake_case)







   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						Dict					,						_snake_case		:						List[Tuple] = None):

        """simple docstring"""


        UpperCAmelCase_          =							outputs.logits

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

             if is_torch_tensor(_snake_case):
                  UpperCAmelCase_          =							target_sizes.numpy()

             UpperCAmelCase_          =							[]

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

        return semantic_segmentation







import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   a_    					=       argparse.ArgumentParser(
       description=(
           'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
           ' Distillation'
       )
   )
   parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
   parser.add_argument('--model_name', default='roberta-large', type=str)
   parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
   parser.add_argument('--vocab_transform', action='store_true')
   a_    					=       parser.parse_args()

   if args.model_type == "roberta":
      a_    					=       RobertaForMaskedLM.from_pretrained(args.model_name)
      a_    					=       'roberta'
   elif args.model_type == "gpt2":
      a_    					=       GPTaLMHeadModel.from_pretrained(args.model_name)
      a_    					=       'transformer'

   a_    					=       model.state_dict()
   a_    					=       {}

   # Embeddings #
   if args.model_type == "gpt2":
      for param_name in ["wte.weight", "wpe.weight"]:
         a_    					=       state_dict[F"""{prefix}.{param_name}"""]
   else:
      for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
         a_    					=       F"""{prefix}.embeddings.{w}.weight"""
         a_    					=       state_dict[param_name]
      for w in ["weight", "bias"]:
         a_    					=       F"""{prefix}.embeddings.LayerNorm.{w}"""
         a_    					=       state_dict[param_name]

    # Transformer Blocks #
   a_    					=       0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      if args.model_type == "gpt2":
         for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
            for w in ["weight", "bias"]:
               a_    					=       state_dict[
                   F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
               ]
         a_    					=       state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
      else:
         for layer in [
             "attention.self.query",
             "attention.self.key",
             "attention.self.value",
             "attention.output.dense",
             "attention.output.LayerNorm",
             "intermediate.dense",
             "output.dense",
             "output.LayerNorm",
         ]:
            for w in ["weight", "bias"]:
               a_    					=       state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
               ]
      std_idx += 1

   # Language Modeling Head ###s
   if args.model_type == "roberta":
      for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
         a_    					=       state_dict[F"""{layer}"""]
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            a_    					=       state_dict[F"""lm_head.dense.{w}"""]
            a_    					=       state_dict[F"""lm_head.layer_norm.{w}"""]
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         a_    					=       state_dict[F"""{prefix}.ln_f.{w}"""]
      a_    					=       state_dict['lm_head.weight']

   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)



