
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_						     =    [
    # 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  lowerCamelCase__ (					_a):
	for pegasus_name, hf_name in PATTERNS:
		SCREAMING_SNAKE_CASE   :      List[str]				     =       k.replace(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_)
	return k

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

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      str				     =       Path(SCREAMING_SNAKE_CASE_).parent.name
	SCREAMING_SNAKE_CASE   :      Dict				     =       task_specific_params[f"summarization_{dataset}"]["max_position_embeddings"]
	SCREAMING_SNAKE_CASE   :      Dict				     =       PegasusTokenizer.from_pretrained("sshleifer/pegasus"							,			model_max_length=SCREAMING_SNAKE_CASE_)
	assert tok.model_max_length == desired_max_model_length
	tok.save_pretrained(SCREAMING_SNAKE_CASE_)

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


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


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class _UpperCamelCase							:
			'''simple docstring'''







			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =None  # sigma(t_i)
			@classmethod
			def 							__UpperCamelCase (			cls							: Tuple	)							->       int:

				"""simple docstring"""

				return cls()






@dataclass
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42
			lowerCamelCase__           =42
			lowerCamelCase__           =42






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







			@property
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[int]:

				"""simple docstring"""

				return True
			@register_to_config
			def __init__(			self							: List[str]      ,      a							: float = 0.02      ,      a							: float = 100      ,      a							: float = 1.007      ,      a							: float = 80      ,      a							: float = 0.05      ,      a							: float = 50      ,      )							->       Optional[Any]:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: Dict	)							->       str:

				"""simple docstring"""

				return KarrasVeSchedulerState.create()
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: KarrasVeSchedulerState      ,      a							: int      ,      a							: Tuple = ()	)							->       KarrasVeSchedulerState:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       jnp.arange(0      ,      a	)[::-1].copy()
				SCREAMING_SNAKE_CASE   :      int				     =       [
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=a      ,      schedule=jnp.array(a      ,      dtype=jnp.floataa	)      ,      timesteps=a      ,      )
			def 							__UpperCamelCase (			self							: str      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: random.KeyArray      ,      )							->       Tuple[jnp.ndarray, float]:

				"""simple docstring"""

				if self.config.s_min <= sigma <= self.config.s_max:
					SCREAMING_SNAKE_CASE   :      Dict				     =       min(self.config.s_churn / state.num_inference_steps      ,      2**0.5 - 1	)
				else:
					SCREAMING_SNAKE_CASE   :      Any				     =       0

				# sample eps ~ N(0, S_noise^2 * I)
				SCREAMING_SNAKE_CASE   :      Any				     =       random.split(a      ,      num=1	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.config.s_noise * random.normal(key=a      ,      shape=sample.shape	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       sigma + gamma * sigma
				SCREAMING_SNAKE_CASE   :      str				     =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: float      ,      a							: jnp.ndarray      ,      a							: bool = True      ,      )							->       Union[FlaxKarrasVeOutput, Tuple]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       sample_hat + sigma_hat * model_output
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (sample_hat - pred_original_sample) / sigma_hat
				SCREAMING_SNAKE_CASE   :      str				     =       sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
					return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=a      ,      derivative=a      ,      state=a	)
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: float      ,      a							: jnp.ndarray      ,      a							: jnp.ndarray      ,      a							: jnp.ndarray      ,      a							: bool = True      ,      )							->       Union[FlaxKarrasVeOutput, Tuple]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       sample_prev + sigma_prev * model_output
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       (sample_prev - pred_original_sample) / sigma_prev
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
					return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=a      ,      derivative=a      ,      state=a	)






			def 							__UpperCamelCase (			self							: int      ,      a							: KarrasVeSchedulerState      ,      a							: Optional[Any]      ,      a							: int      ,      a							: Dict	)							->       List[str]:

				"""simple docstring"""

				raise NotImplementedError()