


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, TensorType

UpperCAmelCase_   :				int         = logging.get_logger(__name__)

UpperCAmelCase_   :				List[Any]         = {
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}
class 				SCREAMING_SNAKE_CASE__						(       __SCREAMING_SNAKE_CASE    ):
      snake_case__       :  Optional[Any]       =		'''imagegpt'''
      snake_case__       :  int       =		['''past_key_values''']
      snake_case__       :  List[Any]       =		{
          '''hidden_size''': '''n_embd''',
          '''max_position_embeddings''': '''n_positions''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }
      def __init__( self       : str  ,							SCREAMING_SNAKE_CASE__       : int=5_1_2 + 1  ,							SCREAMING_SNAKE_CASE__       : List[str]=3_2 * 3_2  ,							SCREAMING_SNAKE_CASE__       : int=5_1_2  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=2_4  ,							SCREAMING_SNAKE_CASE__       : List[str]=8  ,							SCREAMING_SNAKE_CASE__       : Dict=None  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]="quick_gelu"  ,							SCREAMING_SNAKE_CASE__       : str=0.1  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=0.1  ,							SCREAMING_SNAKE_CASE__       : List[str]=0.1  ,							SCREAMING_SNAKE_CASE__       : str=1E-5  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=0.02  ,							SCREAMING_SNAKE_CASE__       : int=True  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=True  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=False  ,							SCREAMING_SNAKE_CASE__       : List[Any]=False  ,							SCREAMING_SNAKE_CASE__       : str=False  ,							**SCREAMING_SNAKE_CASE__       : Tuple  ,							)	->    Optional[Any]:
             a_      :	Optional[int]							=						vocab_size
             a_      :	List[Any]							=						n_positions
             a_      :	Any							=						n_embd
             a_      :	List[Any]							=						n_layer
             a_      :	Optional[int]							=						n_head
             a_      :	Union[str, Any]							=						n_inner
             a_      :	Tuple							=						activation_function
             a_      :	Union[str, Any]							=						resid_pdrop
             a_      :	Union[str, Any]							=						embd_pdrop
             a_      :	Tuple							=						attn_pdrop
             a_      :	List[Any]							=						layer_norm_epsilon
             a_      :	Union[str, Any]							=						initializer_range
             a_      :	Any							=						scale_attn_weights
             a_      :	int							=						use_cache
             a_      :	Tuple							=						scale_attn_by_inverse_layer_idx
             a_      :	Optional[Any]							=						reorder_and_upcast_attn
             a_      :	Dict							=						tie_word_embeddings

             super().__init__(tie_word_embeddings=_a  ,							**_a  )
class 				SCREAMING_SNAKE_CASE__						(       __SCREAMING_SNAKE_CASE    ):
      @property
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    List[str]:
             return OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'sequence'}),
                 ]  )

      def  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : "FeatureExtractionMixin"  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : int = -1  ,							SCREAMING_SNAKE_CASE__       : bool = False  ,							SCREAMING_SNAKE_CASE__       : Optional["TensorType"] = None  ,							SCREAMING_SNAKE_CASE__       : int = 3  ,							SCREAMING_SNAKE_CASE__       : int = 3_2  ,							SCREAMING_SNAKE_CASE__       : int = 3_2  ,							)	->    List[Any]:
             a_      :	Any							=						self._generate_dummy_images(_a  ,							_a  ,							_a  ,							_a  )
             a_      :	List[Any]							=						dict(preprocessor(images=_a  ,							return_tensors=_a  )  )

             return inputs






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


UpperCAmelCase_   :				Optional[int]						      = [
    # 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  SCREAMING_SNAKE_CASE_		(    __A				:    Any       )			->	Union[str, Any]:

       """simple docstring"""

       for pegasus_name, hf_name in PATTERNS:
              a_      :	Union[str, Any]							=						k.replace(__A						,					__A       )
       return k







def  SCREAMING_SNAKE_CASE_		(    __A				:    dict						,					__A				:    dict       )			->	PegasusForConditionalGeneration:

       """simple docstring"""

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

       """simple docstring"""

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

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

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


if __name__ == "__main__":
   UpperCAmelCase_   :				Dict						      = 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.')
   UpperCAmelCase_   :				str						      = parser.parse_args()
   if args.save_dir is None:
      UpperCAmelCase_   :				str						      = Path(args.tf_ckpt_path).parent.name
      UpperCAmelCase_   :				Optional[int]						      = os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


