
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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase__	=							{"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							["YolosFeatureExtractor"]
		lowerCamelCase__	=							["YolosImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							[
		    "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "YolosForObjectDetection",
		    "YolosModel",
		    "YolosPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_yolos import YolosFeatureExtractor
				from .image_processing_yolos import YolosImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_yolos import (
				    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    YolosForObjectDetection,
				    YolosModel,
				    YolosPreTrainedModel,
				)


else:
		import sys

		lowerCamelCase__	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



