




"""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


A_      =    [
    # 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__	(snake_case__							: int   ):
  """simple docstring"""

  for pegasus_name, hf_name in PATTERNS:
    _snake_case							:		Any								=  k.replace(snake_case__		,       snake_case__   )
  return k


def  UpperCAmelCase__	(snake_case__							: dict		,       snake_case__							: dict   ):
  """simple docstring"""

  _snake_case							:		str								=  DEFAULTS.copy()
  cfg_kwargs.update(snake_case__   )
  _snake_case							:		Union[str, Any]								=  PegasusConfig(**snake_case__   )
  _snake_case							:		Union[str, Any]								=  PegasusForConditionalGeneration(snake_case__   )
  _snake_case							:		str								=  torch_model.model.state_dict()
  _snake_case							:		int								=  {}
  for k, v in tf_weights.items():
    _snake_case							:		Optional[int]								=  rename_state_dict_key(snake_case__   )
    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:
      _snake_case							:		Dict								=  v.T
    _snake_case							:		Dict								=  torch.tensor(snake_case__		,       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
  _snake_case							:		str								=  torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]   )
  _snake_case							:		Dict								=  mapping["""shared.weight"""]
  _snake_case							:		int								=  mapping["""shared.weight"""]
  _snake_case							:		List[Any]								=  {k: torch.zeros_like(snake_case__   ) for k, v in sd.items() if k.endswith("""bias"""   ) and k not in mapping}
  mapping.update(**snake_case__   )
  _snake_case		, _snake_case							:		Tuple								=  torch_model.model.load_state_dict(snake_case__		,       strict=snake_case__   )
  _snake_case							:		Optional[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__	(snake_case__							: List[str]="./ckpt/aeslc/model.ckpt-32000"   ):
  """simple docstring"""

  _snake_case							:		Any								=  tf.train.list_variables(snake_case__   )
  _snake_case							:		Any								=  {}
  _snake_case							:		Dict								=  ["""Adafactor""", """global_step"""]
  for name, shape in tqdm(snake_case__		,       desc="""converting tf checkpoint to dict"""   ):
    _snake_case							:		Optional[Any]								=  any(pat in name for pat in ignore_name   )
    if skip_key:
      continue
    _snake_case							:		int								=  tf.train.load_variable(snake_case__		,       snake_case__   )
    _snake_case							:		str								=  array
  return tf_weights


def  UpperCAmelCase__	(snake_case__							: str		,       snake_case__							: str   ):
  """simple docstring"""

  _snake_case							:		Tuple								=  Path(snake_case__   ).parent.name
  _snake_case							:		str								=  task_specific_params[F"summarization_{dataset}"]["""max_position_embeddings"""]
  _snake_case							:		Union[str, Any]								=  PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""		,       model_max_length=snake_case__   )
  assert tok.model_max_length == desired_max_model_length
  tok.save_pretrained(snake_case__   )

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


if __name__ == "__main__":
     A_      =    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_      =    parser.parse_args()
     if args.save_dir is None:
          A_      =    Path(args.tf_ckpt_path).parent.name
          A_      =    os.path.join('''pegasus''', dataset)
     convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








"""simple docstring"""


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
A_      =    logging.get_logger()






@dataclass
class  lowercase:







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = field(default_factory=__a			)
 lowercase__			 = field(default_factory=__a			)


 def 			UpperCamelCase_       (    self:		Optional[Any],				a_:		Union[str, Any],				a_:		Tensor,				a_:		Tensor		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  len(list(m.modules()		)		) == 1 or isinstance(a_,				nn.Convad		) or isinstance(a_,				nn.BatchNormad		)
   if has_not_submodules:
     self.traced.append(a_		)


 def __call__(    self:		List[Any],				a_:		Tensor		):




   '''simple docstring'''



   for m in self.module.modules():
     self.handles.append(m.register_forward_hook(self._forward_hook		)		)
   self.module(a_		)
   [x.remove() for x in self.handles]
   return self


 @property
 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



   return list(filter(lambda a_		: len(list(x.state_dict().keys()		)		) > 0,				self.traced		)		)







@dataclass
class  lowercase:







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = 42
 lowercase__			 = 0
 lowercase__			 = field(default_factory=__a			)
 lowercase__			 = field(default_factory=__a			)


 def __call__(    self:		Dict,				a_:		Tensor		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  Tracker(self.dest		)(a_		).parametrized
   _snake_case							:		int								=  Tracker(self.src		)(a_		).parametrized

   _snake_case							:		Tuple								=  list(filter(lambda a_		: type(a_		) not in self.src_skip,				a_		)		)
   _snake_case							:		Union[str, Any]								=  list(filter(lambda a_		: type(a_		) not in self.dest_skip,				a_		)		)

   if len(a_		) != len(a_		):
     raise Exception(
         f"Numbers of operations are different. Source module has {len(a_		)} operations while"
         f" destination module has {len(a_		)}."		)

   for dest_m, src_m in zip(a_,				a_		):
     dest_m.load_state_dict(src_m.state_dict()		)
     if self.verbose == 1:
       print(f"Transfered from={src_m} to={dest_m}"		)







def  UpperCAmelCase__	(snake_case__							: str		,       snake_case__							: ResNetConfig		,       snake_case__							: Path		,       snake_case__							: bool = True   ):
  """simple docstring"""

  print(F"Converting {name}..."   )
  with torch.no_grad():
    _snake_case							:		Dict								=  timm.create_model(snake_case__		,       pretrained=snake_case__   ).eval()
    _snake_case							:		List[Any]								=  ResNetForImageClassification(snake_case__   ).eval()
    _snake_case							:		List[str]								=  ModuleTransfer(src=snake_case__		,       dest=snake_case__   )
    _snake_case							:		Optional[Any]								=  torch.randn((1, 3, 2_24, 2_24)   )
    module_transfer(snake_case__   )

  assert torch.allclose(from_model(snake_case__   )		,       our_model(snake_case__   ).logits   ), "The model logits don't match the original one."

  _snake_case							:		Optional[int]								=  F"resnet{'-'.join(name.split('resnet'   )   )}"
  print(snake_case__   )

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name		,       commit_message="""Add model"""		,       use_temp_dir=snake_case__		,       )

    # we can use the convnext one
    _snake_case							:		Union[str, Any]								=  AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""   )
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name		,       commit_message="""Add image processor"""		,       use_temp_dir=snake_case__		,       )

    print(F"Pushed {checkpoint_name}"   )


def  UpperCAmelCase__	(snake_case__							: Path		,       snake_case__							: str = None		,       snake_case__							: bool = True   ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  """imagenet-1k-id2label.json"""
  _snake_case							:		Optional[Any]								=  10_00
  _snake_case							:		str								=  (1, num_labels)

  _snake_case							:		List[Any]								=  """huggingface/label-files"""
  _snake_case							:		Union[str, Any]								=  num_labels
  _snake_case							:		Optional[int]								=  json.load(open(hf_hub_download(snake_case__		,       snake_case__		,       repo_type="""dataset"""   )		,       """r"""   )   )
  _snake_case							:		Union[str, Any]								=  {int(snake_case__   ): v for k, v in idalabel.items()}

  _snake_case							:		str								=  idalabel
  _snake_case							:		Union[str, Any]								=  {v: k for k, v in idalabel.items()}

  _snake_case							:		Tuple								=  partial(snake_case__		,       num_labels=snake_case__		,       idalabel=snake_case__		,       labelaid=snake_case__   )

  _snake_case							:		Optional[int]								=  {
      """resnet18""": ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]		,       hidden_sizes=[64, 1_28, 2_56, 5_12]		,       layer_type="""basic"""   ),
      """resnet26""": ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
      """resnet34""": ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]		,       hidden_sizes=[64, 1_28, 2_56, 5_12]		,       layer_type="""basic"""   ),
      """resnet50""": ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
      """resnet101""": ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
      """resnet152""": ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
  }

  if model_name:
    convert_weight_and_push(snake_case__		,       names_to_config[model_name]		,       snake_case__		,       snake_case__   )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )
  return config, expected_shape


if __name__ == "__main__":
     A_      =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--model_name''',
         default=None,
         type=str,
         help=(
             '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
             ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
         ),
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default=None,
         type=Path,
         required=True,
         help='''Path to the output PyTorch model directory.''',
     )
     parser.add_argument(
         '''--push_to_hub''',
         default=True,
         type=bool,
         required=False,
         help='''If True, push model and image processor to the hub.''',
     )

     A_      =    parser.parse_args()
     A_      =    args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)


