


'''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_  :				Dict						= [
    # 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:
  lowerCamelCase_      =  k.replace(UpperCAmelCase_					,				UpperCAmelCase_		)
 return k





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





def __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						str		):
 # save tokenizer first
 lowerCamelCase_      =  Path(UpperCAmelCase_		).parent.name
 lowerCamelCase_      =  task_specific_params[F'''summarization_{dataset}''']["max_position_embeddings"]
 lowerCamelCase_      =  PegasusTokenizer.from_pretrained("sshleifer/pegasus"					,				model_max_length=UpperCAmelCase_		)
 assert tok.model_max_length == desired_max_model_length
 tok.save_pretrained(UpperCAmelCase_		)

 # convert model
 lowerCamelCase_      =  get_tf_weights_as_numpy(UpperCAmelCase_		)
 lowerCamelCase_      =  task_specific_params[F'''summarization_{dataset}''']
 if dataset == "large":
  lowerCamelCase_      =  task_specific_params
 lowerCamelCase_      =  convert_pegasus(UpperCAmelCase_					,				UpperCAmelCase_		)
 torch_model.save_pretrained(UpperCAmelCase_		)
 lowerCamelCase_      =  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_  :				Tuple						= 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_  :				Dict						= parser.parse_args()
     if args.save_dir is None:
          a_  :				Union[str, Any]						= Path(args.tf_ckpt_path).parent.name
          a_  :				List[str]						= os.path.join("""pegasus""", dataset)
     convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowercase: Union[str, Any]      			=		get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
				_lowercase: List[str]      			=		json.load(f)



@require_torch
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return FSMTTokenizer.from_pretrained(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_		).to(lowerCamelCase_		)
						if torch_device == "cuda":
											model.half()
						return model




	@parameterized.expand(
	    [
	        ["en-ru", 26.0],
	        ["ru-en", 22.0],
	        ["en-de", 22.0],
	        ["de-en", 29.0],
	    ]		)
	@slow
	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   F'''facebook/wmt19-{pair}'''
						a					     =   self.get_tokenizer(lowerCamelCase_		)
						a					     =   self.get_model(lowerCamelCase_		)

						a					     =   bleu_data[pair]["src"]
						a					     =   bleu_data[pair]["tgt"]

						a					     =   tokenizer(lowerCamelCase_		,       return_tensors="pt"		,       truncation=lowerCamelCase_		,       padding="longest"		).to(lowerCamelCase_		)
						a					     =   model.generate(
						    input_ids=batch.input_ids		,       num_beams=8		,       )
						a					     =   tokenizer.batch_decode(
						    lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       clean_up_tokenization_spaces=lowerCamelCase_		)
						a					     =   calculate_bleu(lowerCamelCase_		,       lowerCamelCase_		)
						print(lowerCamelCase_		)
						self.assertGreaterEqual(scores["bleu"]		,       lowerCamelCase_		)
