



"""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:     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    _snake_case		(		UpperCamelCase		:	List[Any]		):
 for pegasus_name, hf_name in PATTERNS:
  UpperCAmelCase			:      Tuple			=			k.replace(UpperCamelCase    ,	UpperCamelCase		)
 return k

def    _snake_case		(		UpperCamelCase		:	dict    ,	UpperCamelCase		:	dict		):
 UpperCAmelCase			:      Union[str, Any]			=			DEFAULTS.copy()
 cfg_kwargs.update(UpperCamelCase		)
 UpperCAmelCase			:      Union[str, Any]			=			PegasusConfig(**UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			PegasusForConditionalGeneration(UpperCamelCase		)
 UpperCAmelCase			:      List[Any]			=			torch_model.model.state_dict()
 UpperCAmelCase			:      List[str]			=			{}
 for k, v in tf_weights.items():
  UpperCAmelCase			:      str			=			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			:      Optional[int]			=			v.T
  UpperCAmelCase			:      Dict			=			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			:      Optional[int]			=			torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]		)
 UpperCAmelCase			:      Union[str, Any]			=			mapping["""shared.weight"""]
 UpperCAmelCase			:      Union[str, Any]			=			mapping["""shared.weight"""]
 UpperCAmelCase			:      Optional[int]			=			{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			:      List[str]			=			torch_model.model.load_state_dict(UpperCamelCase    ,	strict=UpperCamelCase		)
 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    _snake_case		(		UpperCamelCase		:	Optional[int]="./ckpt/aeslc/model.ckpt-32000"		):
 UpperCAmelCase			:      Union[str, Any]			=			tf.train.list_variables(UpperCamelCase		)
 UpperCAmelCase			:      Union[str, Any]			=			{}
 UpperCAmelCase			:      int			=			["""Adafactor""", """global_step"""]
 for name, shape in tqdm(UpperCamelCase    ,	desc="""converting tf checkpoint to dict"""		):
  UpperCAmelCase			:      int			=			any(pat in name for pat in ignore_name		)
  if skip_key:
   continue
  UpperCAmelCase			:      int			=			tf.train.load_variable(UpperCamelCase    ,	UpperCamelCase		)
  UpperCAmelCase			:      Tuple			=			array
 return tf_weights

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str		):
 # save tokenizer first
 UpperCAmelCase			:      Any			=			Path(UpperCamelCase		).parent.name
 UpperCAmelCase			:      Optional[Any]			=			task_specific_params[F"summarization_{dataset}"]["""max_position_embeddings"""]
 UpperCAmelCase			:      Dict			=			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			:      str			=			get_tf_weights_as_numpy(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			task_specific_params[F"summarization_{dataset}"]
 if dataset == "large":
  UpperCAmelCase			:      int			=			task_specific_params
 UpperCAmelCase			:      Union[str, Any]			=			convert_pegasus(UpperCamelCase    ,	UpperCamelCase		)
 torch_model.save_pretrained(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			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:     Any										=	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:     List[Any]										=	parser.parse_args()
      if args.save_dir is None:
            A:     int										=	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)





"""simple docstring"""
import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     Any		=		XLMTokenizer
   __lowerCAmelCase						:     List[Any]		=		False

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    UpperCAmelCase			:      Optional[int]			=			[
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """w</w>""",
        """r</w>""",
        """t</w>""",
        """lo""",
        """low""",
        """er</w>""",
        """low</w>""",
        """lowest</w>""",
        """newer</w>""",
        """wider</w>""",
        """<unk>""",
    ]
    UpperCAmelCase			:      str			=			dict(zip(_SCREAMING_SNAKE_CASE   ,  range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
    UpperCAmelCase			:      List[Any]			=			["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

    UpperCAmelCase			:      Optional[Any]			=			os.path.join(self.tmpdirname   ,  VOCAB_FILES_NAMES["""vocab_file"""]		)
    UpperCAmelCase			:      Optional[Any]			=			os.path.join(self.tmpdirname   ,  VOCAB_FILES_NAMES["""merges_file"""]		)
    with open(self.vocab_file   ,  """w"""		) as fp:
     fp.write(json.dumps(_SCREAMING_SNAKE_CASE		)		)
    with open(self.merges_file   ,  """w"""		) as fp:
     fp.write("""\n""".join(_SCREAMING_SNAKE_CASE		)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			"""lower newer"""
    UpperCAmelCase			:      Dict			=			"""lower newer"""
    return input_text, output_text

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			XLMTokenizer(self.vocab_file   ,  self.merges_file		)

    UpperCAmelCase			:      Optional[int]			=			"""lower"""
    UpperCAmelCase			:      Optional[int]			=			["""low""", """er</w>"""]
    UpperCAmelCase			:      List[Any]			=			tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
    self.assertListEqual(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			tokens + ["""<unk>"""]
    UpperCAmelCase			:      List[Any]			=			[14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE		)




   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""		)

    UpperCAmelCase			:      int			=			tokenizer.encode("""sequence builders"""   ,  add_special_tokens=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			tokenizer.encode("""multi-sequence build"""   ,  add_special_tokens=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    assert encoded_sentence == [0] + text + [1]
    assert encoded_pair == [0] + text + [1] + text_a + [1]
