

'''simple docstring'''

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


lowercase__         =      [
    # 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 				UpperCamelCase(					UpperCAmelCase_     ):
 for pegasus_name, hf_name in PATTERNS:
  UpperCAmelCase	:     Dict						 =			k.replace(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return k




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Dict						 =			DEFAULTS.copy()
 cfg_kwargs.update(UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			PegasusConfig(**UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			PegasusForConditionalGeneration(UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			torch_model.model.state_dict()
 UpperCAmelCase	:     Tuple						 =			{}
 for k, v in tf_weights.items():
  UpperCAmelCase	:     List[Any]						 =			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	:     List[Any]						 =			v.T
  UpperCAmelCase	:     Optional[int]						 =			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	:     List[str]						 =			torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]     )
 UpperCAmelCase	:     Optional[Any]						 =			mapping['shared.weight']
 UpperCAmelCase	:     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	:     Dict						 =			torch_model.model.load_state_dict(UpperCAmelCase_  ,	strict=UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			[
     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 				UpperCamelCase(					UpperCAmelCase_="./ckpt/aeslc/model.ckpt-32000"     ):
 UpperCAmelCase	:     List[str]						 =			tf.train.list_variables(UpperCAmelCase_     )
 UpperCAmelCase	:     List[Any]						 =			{}
 UpperCAmelCase	:     Dict						 =			['Adafactor', 'global_step']
 for name, shape in tqdm(UpperCAmelCase_  ,	desc='converting tf checkpoint to dict'     ):
  UpperCAmelCase	:     str						 =			any(pat in name for pat in ignore_name     )
  if skip_key:
   continue
  UpperCAmelCase	:     Any						 =			tf.train.load_variable(UpperCAmelCase_  ,	UpperCAmelCase_     )
  UpperCAmelCase	:     int						 =			array
 return tf_weights




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 # save tokenizer first
 UpperCAmelCase	:     Dict						 =			Path(UpperCAmelCase_     ).parent.name
 UpperCAmelCase	:     Dict						 =			task_specific_params[F"""summarization_{dataset}"""]['max_position_embeddings']
 UpperCAmelCase	:     Tuple						 =			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	:     Any						 =			get_tf_weights_as_numpy(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			task_specific_params[F"""summarization_{dataset}"""]
 if dataset == "large":
  UpperCAmelCase	:     List[str]						 =			task_specific_params
 UpperCAmelCase	:     Union[str, Any]						 =			convert_pegasus(UpperCAmelCase_  ,	UpperCAmelCase_     )
 torch_model.save_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     List[Any]						 =			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__":
 lowercase__         =      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.")
 lowercase__         =      parser.parse_args()
 if args.save_dir is None:
  lowercase__         =      Path(args.tf_ckpt_path).parent.name
  lowercase__         =      os.path.join("pegasus", dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)







'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import 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_vision_available, logging


if is_vision_available():
 import PIL


lowercase__         =      logging.get_logger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			b.T
 UpperCAmelCase	:     Optional[int]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=1     )
 UpperCAmelCase	:     List[Any]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=0     )
 UpperCAmelCase	:     List[str]						 =			np.matmul(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			aa[:, None] - 2 * ab + ba[None, :]
 return d




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			x.reshape(-1  ,	3     )
 UpperCAmelCase	:     Optional[int]						 =			squared_euclidean_distance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return np.argmin(UpperCAmelCase_  ,	axis=1     )




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[Any]  		=  ["""pixel_values"""]


     def __init__(		self			:					List[Any]					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					bool = True					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Any						 =			size if size is not None else {'height': 256, 'width': 256}
      UpperCAmelCase	:     List[Any]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     str						 =			np.array(lowercase_	) if clusters is not None else None
      UpperCAmelCase	:     Any						 =			do_resize
      UpperCAmelCase	:     List[Any]						 =			size
      UpperCAmelCase	:     Any						 =			resample
      UpperCAmelCase	:     Dict						 =			do_normalize
      UpperCAmelCase	:     List[Any]						 =			do_color_quantize


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Any					,      )						->     np.ndarray:
      UpperCAmelCase	:     Dict						 =			get_size_dict(lowercase_	)
      if "height" not in size or "width" not in size:
       raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""	)
      return resize(
          lowercase_					,      size=(size['height'], size['width'])					,      resample=lowercase_					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      )						->     np.ndarray:
      UpperCAmelCase	:     int						 =			rescale(image=lowercase_					,      scale=1 / 127.5					,      data_format=lowercase_	)
      UpperCAmelCase	:     Dict						 =			image - 1
      return image




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					ImageInput					,      lowercase_			:					bool = None					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[bool] = None					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					Optional[Union[str, TensorType]] = None					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					,      **lowercase_			:					List[str]					,      )						->     PIL.Image.Image:
      UpperCAmelCase	:     Optional[int]						 =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase	:     Optional[Any]						 =			size if size is not None else self.size
      UpperCAmelCase	:     Optional[int]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     Any						 =			resample if resample is not None else self.resample
      UpperCAmelCase	:     Optional[Any]						 =			do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase	:     str						 =			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCAmelCase	:     Optional[int]						 =			clusters if clusters is not None else self.clusters
      UpperCAmelCase	:     List[str]						 =			np.array(lowercase_	)

      UpperCAmelCase	:     int						 =			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 or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.'	)

      if do_color_quantize and clusters is None:
       raise ValueError('Clusters must be specified if do_color_quantize is True.'	)

      # All transformations expect numpy arrays.
      UpperCAmelCase	:     Dict						 =			[to_numpy_array(lowercase_	) for image in images]

      if do_resize:
       UpperCAmelCase	:     List[Any]						 =			[self.resize(image=lowercase_					,      size=lowercase_					,      resample=lowercase_	) for image in images]

      if do_normalize:
       UpperCAmelCase	:     Tuple						 =			[self.normalize(image=lowercase_	) for image in images]

      if do_color_quantize:
       UpperCAmelCase	:     List[str]						 =			[to_channel_dimension_format(lowercase_					,      ChannelDimension.LAST	) for image in images]
       # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
       UpperCAmelCase	:     int						 =			np.array(lowercase_	)
       UpperCAmelCase	:     str						 =			color_quantize(lowercase_					,      lowercase_	).reshape(images.shape[:-1]	)

       # flatten to (batch_size, height*width)
       UpperCAmelCase	:     Optional[int]						 =			images.shape[0]
       UpperCAmelCase	:     Union[str, Any]						 =			images.reshape(lowercase_					,      -1	)

       # We need to convert back to a list of images to keep consistent behaviour across processors.
       UpperCAmelCase	:     int						 =			list(lowercase_	)
      else:
       UpperCAmelCase	:     Dict						 =			[to_channel_dimension_format(lowercase_					,      lowercase_	) for image in images]

      UpperCAmelCase	:     Any						 =			{'input_ids': images}
      return BatchFeature(data=lowercase_					,      tensor_type=lowercase_	)




