

'''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       :	Optional[Any]											= k.replace(__lowercase  ,       __lowercase	)
 return k




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




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_	):
 UpperCAmelCase       :	Optional[Any]											= Path(__lowercase	).parent.name
 UpperCAmelCase       :	Dict											= task_specific_params[F"""summarization_{dataset}"""]['max_position_embeddings']
 UpperCAmelCase       :	Union[str, Any]											= PegasusTokenizer.from_pretrained('sshleifer/pegasus'  ,       model_max_length=__lowercase	)
 assert tok.model_max_length == desired_max_model_length
 tok.save_pretrained(__lowercase	)

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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__							:							List[Any] ={
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__							:							Optional[int] =[
        '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TimesformerModel''',
        '''TimesformerForVideoClassification''',
        '''TimesformerPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timesformer import (
            TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TimesformerForVideoClassification,
            TimesformerModel,
            TimesformerPreTrainedModel,
        )

else:
    import sys

    a__							:							Optional[int] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
