
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


lowerCamelCase__	=							[
    # 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 							__A(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		for pegasus_name, hf_name in PATTERNS:
				_UpperCamelCase						    =	k.replace(lowerCAmelCase						,  lowerCAmelCase )
		return k


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				PegasusForConditionalGeneration:

		"""simple docstring"""






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

		"""simple docstring"""






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


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				str:

		"""simple docstring"""






		_UpperCamelCase						    =	Path(lowerCAmelCase ).parent.name
		_UpperCamelCase						    =	task_specific_params[F'summarization_{dataset}']["""max_position_embeddings"""]
		_UpperCamelCase						    =	PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""						,  model_max_length=lowerCAmelCase )
		assert tok.model_max_length == desired_max_model_length
		tok.save_pretrained(lowerCAmelCase )

		# convert model
		_UpperCamelCase						    =	get_tf_weights_as_numpy(lowerCAmelCase )
		_UpperCamelCase						    =	task_specific_params[F'summarization_{dataset}']
		if dataset == "large":
				_UpperCamelCase						    =	task_specific_params
		_UpperCamelCase						    =	convert_pegasus(lowerCAmelCase						,  lowerCAmelCase )
		torch_model.save_pretrained(lowerCAmelCase )
		_UpperCamelCase						    =	torch_model.state_dict()
		sd.pop("""model.decoder.embed_positions.weight""" )
		sd.pop("""model.encoder.embed_positions.weight""" )
		torch.save(lowerCAmelCase						,  Path(lowerCAmelCase ) / """pytorch_model.bin""" )


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





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		return ConvertCommand(
		    args.model_type						,  args.tf_checkpoint						,  args.pytorch_dump_output						,  args.config						,  args.finetuning_task_name )


lowerCamelCase__	=							"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"







class        lowerCAmelCase__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	parser.add_parser(
								    """convert"""       ,							help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."""       ,							)
								train_parser.add_argument("""--model_type"""       ,							type=a       ,							required=a       ,							help="""Model's type."""				)
								train_parser.add_argument(
								    """--tf_checkpoint"""       ,							type=a       ,							required=a       ,							help="""TensorFlow checkpoint path or folder."""				)
								train_parser.add_argument(
								    """--pytorch_dump_output"""       ,							type=a       ,							required=a       ,							help="""Path to the PyTorch saved model output."""				)
								train_parser.add_argument("""--config"""       ,							type=a       ,							default=""""""       ,							help="""Configuration file path or folder."""				)
								train_parser.add_argument(
								    """--finetuning_task_name"""       ,							type=a       ,							default=a       ,							help="""Optional fine-tuning task name if the TF model was a finetuned model."""       ,							)
								train_parser.set_defaults(func=a				)

						def __init__(			self       ,							a       ,							a       ,							a       ,							a       ,							a       ,							*a       ,							)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	logging.get_logger("""transformers-cli/converting"""				)

								self._logger.info(F'Loading model {model_type}'				)
								_UpperCamelCase						    =	model_type
								_UpperCamelCase						    =	tf_checkpoint
								_UpperCamelCase						    =	pytorch_dump_output
								_UpperCamelCase						    =	config
								_UpperCamelCase						    =	finetuning_task_name







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								if self._model_type == "albert":
										try:
												from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "bert":
										try:
												from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "funnel":
										try:
												from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "t5":
										try:
												from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "gpt":
										from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
										    convert_openai_checkpoint_to_pytorch,
										)

										convert_openai_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "transfo_xl":
										try:
												from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
												    convert_transfo_xl_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										if "ckpt" in self._tf_checkpoint.lower():
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										else:
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										convert_transfo_xl_checkpoint_to_pytorch(
										    a       ,							self._config       ,							self._pytorch_dump_output       ,							a				)
								elif self._model_type == "gpt2":
										try:
												from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
												    convert_gpta_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "xlnet":
										try:
												from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
												    convert_xlnet_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_xlnet_checkpoint_to_pytorch(
										    self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output       ,							self._finetuning_task_name				)
								elif self._model_type == "xlm":
										from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
										    convert_xlm_checkpoint_to_pytorch,
										)

										convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._pytorch_dump_output				)
								elif self._model_type == "lxmert":
										from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
										    convert_lxmert_checkpoint_to_pytorch,
										)

										convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._pytorch_dump_output				)
								elif self._model_type == "rembert":
										from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
										    convert_rembert_tf_checkpoint_to_pytorch,
										)

										convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								else:
										raise ValueError(
										    """--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"""				)



