






"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
   import PIL

A_		:		Union[str, Any]		     =			logging.get_logger(__name__)

def   snake_case (UpperCAmelCase__  )	->    List[List[ImageInput]]:
  if isinstance(UpperCAmelCase__     ,   (list, tuple)  ) and isinstance(videos[0]     ,   (list, tuple)  ) and is_valid_image(videos[0][0]  ):
    return videos

  elif isinstance(UpperCAmelCase__     ,   (list, tuple)  ) and is_valid_image(videos[0]  ):
    return [videos]

  elif is_valid_image(UpperCAmelCase__  ):
    return [[videos]]

  raise ValueError(F'''Could not make batched video from {videos}'''  )




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		List[str]									=['''pixel_values''']





       def __init__( self  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = PILImageResampling.BILINEAR  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      _lowerCamelCase = 1 / 2_5_5  ,      _lowerCamelCase = True  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         super().__init__(**_lowerCamelCase							)
         UpperCamelCase_: Any   				=    size if size is not None else {'shortest_edge': 2_5_6}
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase							)
         UpperCamelCase_: Optional[Any]   				=    crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: Optional[int]   				=    get_size_dict(_lowerCamelCase  ,      param_name='crop_size'							)

         UpperCamelCase_: List[Any]   				=    do_resize
         UpperCamelCase_: str   				=    size
         UpperCamelCase_: Optional[Any]   				=    do_center_crop
         UpperCamelCase_: Optional[int]   				=    crop_size
         UpperCamelCase_: Any   				=    resample
         UpperCamelCase_: Tuple   				=    do_rescale
         UpperCamelCase_: Optional[Any]   				=    rescale_factor
         UpperCamelCase_: Union[str, Any]   				=    offset
         UpperCamelCase_: List[str]   				=    do_normalize
         UpperCamelCase_: Union[str, Any]   				=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCamelCase_: List[Any]   				=    image_std if image_std is not None else IMAGENET_STANDARD_STD





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = PILImageResampling.BILINEAR  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Union[str, Any]   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase							)
         if "shortest_edge" in size:
           UpperCamelCase_: str   				=    get_resize_output_image_size(_lowerCamelCase  ,      size['shortest_edge']  ,      default_to_square=_lowerCamelCase							)
         elif "height" in size and "width" in size:
           UpperCamelCase_: int   				=    (size['height'], size['width'])
         else:
           raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''							)
         return resize(_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[int]   				=    get_size_dict(_lowerCamelCase							)
         if "height" not in size or "width" not in size:
           raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''							)
         return center_crop(_lowerCamelCase  ,      size=(size['height'], size['width'])  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: int   				=    image.astype(np.floataa							)
         if offset:
           UpperCamelCase_: List[Any]   				=    image - (scale / 2)
         return rescale(_lowerCamelCase  ,      scale=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         return normalize(_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = ChannelDimension.FIRST  ,      ):
         if do_resize and size is None or resample is None:
           raise ValueError('Size and resample must be specified if do_resize is True.'							)

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

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

         if do_normalize and (image_mean is None or image_std is None):
           raise ValueError('Image mean and std must be specified if do_normalize is True.'							)

         if offset and not do_rescale:
           raise ValueError('For offset, do_rescale must also be set to True.'							)

         # All transformations expect numpy arrays.
         UpperCamelCase_: Optional[Any]   				=    to_numpy_array(_lowerCamelCase							)

         if do_resize:
           UpperCamelCase_: Dict   				=    self.resize(image=_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase							)

         if do_center_crop:
           UpperCamelCase_: Any   				=    self.center_crop(_lowerCamelCase  ,      size=_lowerCamelCase							)

         if do_rescale:
           UpperCamelCase_: Optional[int]   				=    self.rescale(image=_lowerCamelCase  ,      scale=_lowerCamelCase  ,      offset=_lowerCamelCase							)

         if do_normalize:
           UpperCamelCase_: Optional[Any]   				=    self.normalize(image=_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase							)

         UpperCamelCase_: List[str]   				=    to_channel_dimension_format(_lowerCamelCase  ,      _lowerCamelCase							)
         return image





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = ChannelDimension.FIRST  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Union[str, Any]   				=    do_resize if do_resize is not None else self.do_resize
         UpperCamelCase_: Any   				=    resample if resample is not None else self.resample
         UpperCamelCase_: int   				=    do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCamelCase_: Optional[Any]   				=    do_rescale if do_rescale is not None else self.do_rescale
         UpperCamelCase_: Union[str, Any]   				=    rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCamelCase_: int   				=    offset if offset is not None else self.offset
         UpperCamelCase_: Union[str, Any]   				=    do_normalize if do_normalize is not None else self.do_normalize
         UpperCamelCase_: Any   				=    image_mean if image_mean is not None else self.image_mean
         UpperCamelCase_: List[str]   				=    image_std if image_std is not None else self.image_std

         UpperCamelCase_: Optional[int]   				=    size if size is not None else self.size
         UpperCamelCase_: Tuple   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase							)
         UpperCamelCase_: str   				=    crop_size if crop_size is not None else self.crop_size
         UpperCamelCase_: Dict   				=    get_size_dict(_lowerCamelCase  ,      param_name='crop_size'							)

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

         UpperCamelCase_: Tuple   				=    make_batched(_lowerCamelCase							)

         UpperCamelCase_: Dict   				=    [
             [
                 self._preprocess_image(
                     image=_lowerCamelCase  ,      do_resize=_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase  ,      do_center_crop=_lowerCamelCase  ,      crop_size=_lowerCamelCase  ,      do_rescale=_lowerCamelCase  ,      rescale_factor=_lowerCamelCase  ,      offset=_lowerCamelCase  ,      do_normalize=_lowerCamelCase  ,      image_mean=_lowerCamelCase  ,      image_std=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      )
                 for img in video
             ]
             for video in videos
         ]

         UpperCamelCase_: Optional[Any]   				=    {'pixel_values': videos}
         return BatchFeature(data=_lowerCamelCase  ,      tensor_type=_lowerCamelCase							)







import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


A_		:		Tuple		     =			[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]

def   snake_case (UpperCAmelCase__  )	->    str:
  for pegasus_name, hf_name in PATTERNS:
    UpperCamelCase_: List[str]   				=    k.replace(UpperCAmelCase__     ,   UpperCAmelCase__  )
  return k

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    PegasusForConditionalGeneration:
  UpperCamelCase_: List[str]   				=    DEFAULTS.copy()
  cfg_kwargs.update(UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    PegasusConfig(**UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    PegasusForConditionalGeneration(UpperCAmelCase__  )
  UpperCamelCase_: List[Any]   				=    torch_model.model.state_dict()
  UpperCamelCase_: str   				=    {}
  for k, v in tf_weights.items():
    UpperCamelCase_: Dict   				=    rename_state_dict_key(UpperCAmelCase__  )
    if new_k not in sd:
      raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''  )

    if "dense" in k or "proj" in new_k:
      UpperCamelCase_: int   				=    v.T
    UpperCamelCase_: Union[str, Any]   				=    torch.tensor(UpperCAmelCase__     ,   dtype=sd[new_k].dtype  )
    assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
  # make sure embedding.padding_idx is respected
  UpperCamelCase_: Tuple   				=    torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]  )
  UpperCamelCase_: int   				=    mapping['shared.weight']
  UpperCamelCase_: Union[str, Any]   				=    mapping['shared.weight']
  UpperCamelCase_: Dict   				=    {k: torch.zeros_like(UpperCAmelCase__  ) for k, v in sd.items() if k.endswith('bias'  ) and k not in mapping}
  mapping.update(**UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    torch_model.model.load_state_dict(UpperCAmelCase__     ,   strict=UpperCAmelCase__  )
  UpperCamelCase_: List[str]   				=    [
      k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
  ]
  assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
  assert extra == [], F'''no matches found for the following tf keys {extra}'''
  return torch_model

def   snake_case (UpperCAmelCase__="./ckpt/aeslc/model.ckpt-32000"  )	->    Dict:
  UpperCamelCase_: Union[str, Any]   				=    tf.train.list_variables(UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    {}
  UpperCamelCase_: Dict   				=    ['Adafactor', 'global_step']
  for name, shape in tqdm(UpperCAmelCase__     ,   desc='converting tf checkpoint to dict'  ):
    UpperCamelCase_: Union[str, Any]   				=    any(pat in name for pat in ignore_name  )
    if skip_key:
      continue
    UpperCamelCase_: Dict   				=    tf.train.load_variable(UpperCAmelCase__     ,   UpperCAmelCase__  )
    UpperCamelCase_: Optional[Any]   				=    array
  return tf_weights

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  # save tokenizer first
  UpperCamelCase_: Any   				=    Path(UpperCAmelCase__  ).parent.name
  UpperCamelCase_: Tuple   				=    task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
  UpperCamelCase_: Optional[Any]   				=    PegasusTokenizer.from_pretrained('sshleifer/pegasus'     ,   model_max_length=UpperCAmelCase__  )
  assert tok.model_max_length == desired_max_model_length
  tok.save_pretrained(UpperCAmelCase__  )

  # convert model
  UpperCamelCase_: Optional[Any]   				=    get_tf_weights_as_numpy(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    task_specific_params[F'''summarization_{dataset}''']
  if dataset == "large":
    UpperCamelCase_: Union[str, Any]   				=    task_specific_params
  UpperCamelCase_: Tuple   				=    convert_pegasus(UpperCAmelCase__     ,   UpperCAmelCase__  )
  torch_model.save_pretrained(UpperCAmelCase__  )
  UpperCamelCase_: int   				=    torch_model.state_dict()
  sd.pop('model.decoder.embed_positions.weight'  )
  sd.pop('model.encoder.embed_positions.weight'  )
  torch.save(UpperCAmelCase__     ,   Path(UpperCAmelCase__  ) / 'pytorch_model.bin'  )


if __name__ == "__main__":
   A_		:		str		     =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
   parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
   A_		:		Optional[Any]		     =			parser.parse_args()
   if args.save_dir is None:
      A_		:		Union[str, Any]		     =			Path(args.tf_ckpt_path).parent.name
      A_		:		Optional[Any]		     =			os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)