
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      :				List[str]          = [
    # 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 (      lowercase				)     ->						List[str]:
		'''simple docstring'''

		for pegasus_name, hf_name in PATTERNS:
				UpperCamelCase						=  k.replace(lowercase					, lowercase				)
		return k


def    A (      lowercase					, lowercase				)     ->						PegasusForConditionalGeneration:
		'''simple docstring'''

		UpperCamelCase						=  DEFAULTS.copy()
		cfg_kwargs.update(lowercase				)
		UpperCamelCase						=  PegasusConfig(**lowercase				)
		UpperCamelCase						=  PegasusForConditionalGeneration(lowercase				)
		UpperCamelCase						=  torch_model.model.state_dict()
		UpperCamelCase						=  {}
		for k, v in tf_weights.items():
				UpperCamelCase						=  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						=  v.T
				UpperCamelCase						=  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						=  torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]				)
		UpperCamelCase						=  mapping['shared.weight']
		UpperCamelCase						=  mapping['shared.weight']
		UpperCamelCase						=  {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						=  torch_model.model.load_state_dict(lowercase					, strict=lowercase				)
		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 (      lowercase="./ckpt/aeslc/model.ckpt-32000"				)     ->						Dict:
		'''simple docstring'''

		UpperCamelCase						=  tf.train.list_variables(lowercase				)
		UpperCamelCase						=  {}
		UpperCamelCase						=  ['Adafactor', 'global_step']
		for name, shape in tqdm(lowercase					, 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(lowercase					, lowercase				)
				UpperCamelCase						=  array
		return tf_weights


def    A (      lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase						=  Path(lowercase				).parent.name
		UpperCamelCase						=  task_specific_params[f'''summarization_{dataset}''']['max_position_embeddings']
		UpperCamelCase						=  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						=  get_tf_weights_as_numpy(lowercase				)
		UpperCamelCase						=  task_specific_params[f'''summarization_{dataset}''']
		if dataset == "large":
				UpperCamelCase						=  task_specific_params
		UpperCamelCase						=  convert_pegasus(lowercase					, lowercase				)
		torch_model.save_pretrained(lowercase				)
		UpperCamelCase						=  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__":
					_UpperCAmelCase      :				str          = 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      :				Tuple          = parser.parse_args()
					if args.save_dir is None:
										_UpperCAmelCase      :				Optional[int]          = Path(args.tf_ckpt_path).parent.name
										_UpperCAmelCase      :				Union[str, Any]          = os.path.join("pegasus", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


from collections.abc import Callable


def    A (      lowercase					, lowercase					, lowercase				)     ->						float:
		'''simple docstring'''

		UpperCamelCase						=  a
		UpperCamelCase						=  b
		if function(lowercase				) == 0:  # one of the a or b is a root for the function
				return a
		elif function(lowercase				) == 0:
				return b
		elif (
		    function(lowercase				) * function(lowercase				) > 0
		):  # if none of these are root and they are both positive or negative,
				# then this algorithm can't find the root
				raise ValueError('could not find root in given interval.'				)
		else:
				UpperCamelCase						=  start + (end - start) / 2.0
				while abs(start - mid				) > 10**-7:  # until precisely equals to 10^-7
						if function(lowercase				) == 0:
								return mid
						elif function(lowercase				) * function(lowercase				) < 0:
								UpperCamelCase						=  mid
						else:
								UpperCamelCase						=  mid
						UpperCamelCase						=  start + (end - start) / 2.0
				return mid


def    A (      lowercase				)     ->						float:
		'''simple docstring'''

		return x**3 - 2 * x - 5


if __name__ == "__main__":
					print(bisection(f, 1, 1_000))

					import doctest

					doctest.testmod()
