






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__     :   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 _UpperCAmelCase   (	a						:	Union[str, Any] ):

							for pegasus_name, hf_name in PATTERNS:
														snake_case__	  =       k.replace(a  ,					a )
							return k

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

def _UpperCAmelCase   (	a						:	Any  ,					a						:	Tuple ):
							snake_case__	  =       Path(a ).parent.name
							snake_case__	  =       task_specific_params[F'''summarization_{dataset}''']["""max_position_embeddings"""]
							snake_case__	  =       PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""  ,					model_max_length=a )
							assert tok.model_max_length == desired_max_model_length
							tok.save_pretrained(a )

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


if __name__ == "__main__":
	a__     :   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.""")
	a__     :   Tuple       =     parser.parse_args()
	if args.save_dir is None:
		a__     :   Optional[int]       =     Path(args.tf_ckpt_path).parent.name
		a__     :   Union[str, Any]       =     os.path.join("""pegasus""", dataset)
	convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class    _lowerCAmelCase			(		lowercase_     ,      lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   Optional[int]      =  VQModel
				_lowercase				:   str      =  '''sample'''



				@property
				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    Tuple=(3_2, 3_2)):






											'''simple docstring'''







											snake_case__	  =       4
											snake_case__	  =       3

											snake_case__	  =       floats_tensor((batch_size, num_channels) + sizes).to(UpperCamelCase__)

											return {"sample": image}



				@property
				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											return (3, 3_2, 3_2)



				@property
				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											return (3, 3_2, 3_2)



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       {
											    """block_out_channels""": [3_2, 6_4],
											    """in_channels""": 3,
											    """out_channels""": 3,
											    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
											    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
											    """latent_channels""": 3,
											}
											snake_case__	  =       self.dummy_input
											return init_dict, inputs_dict



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       VQModel.from_pretrained("""fusing/vqgan-dummy"""			,		output_loading_info=UpperCamelCase__)
											self.assertIsNotNone(UpperCamelCase__)
											self.assertEqual(len(loading_info["""missing_keys"""])			,		0)

											model.to(UpperCamelCase__)
											snake_case__	  =       model(**self.dummy_input)

											assert image is not None, "Make sure output is not None"



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       VQModel.from_pretrained("""fusing/vqgan-dummy""")
											model.to(UpperCamelCase__).eval()

											torch.manual_seed(0)
											if torch.cuda.is_available():
																		torch.cuda.manual_seed_all(0)

											snake_case__	  =       torch.randn(1			,		model.config.in_channels			,		model.config.sample_size			,		model.config.sample_size)
											snake_case__	  =       image.to(UpperCamelCase__)
											with torch.no_grad():
																		snake_case__	  =       model(UpperCamelCase__).sample

											snake_case__	  =       output[0, -1, -3:, -3:].flatten().cpu()
											# fmt: off
											snake_case__	  =       torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43])
											# fmt: on
											self.assertTrue(torch.allclose(UpperCamelCase__			,		UpperCamelCase__			,		atol=1E-3))
