
import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)



class    a_ (			unittest.TestCase    ):






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Vector([1, 2, 3]  )
									self.assertEqual(x.component(0  )				,			1  )
									self.assertEqual(x.component(2  )				,			3  )
									_lowercase					       =					Vector()






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Vector([0, 0, 0, 0, 0, 1]  )
									self.assertEqual(str(__UpperCamelCase  )				,			"""(0,0,0,0,0,1)"""  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Vector([1, 2, 3, 4]  )
									self.assertEqual(len(__UpperCamelCase  )				,			4  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Vector([1, 2]  )
									_lowercase					       =					Vector([1, 2, 3, 4, 5]  )
									_lowercase					       =					Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  )
									_lowercase					       =					Vector([1, -1, 1, -1, 2, -3, 4, -5]  )
									self.assertAlmostEqual(x.euclidean_length()				,			2.2_36				,			3  )
									self.assertAlmostEqual(y.euclidean_length()				,			7.4_16				,			3  )
									self.assertEqual(z.euclidean_length()				,			0  )
									self.assertAlmostEqual(w.euclidean_length()				,			7.6_16				,			3  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Vector([1, 2, 3]  )
									_lowercase					       =					Vector([1, 1, 1]  )
									self.assertEqual((x + y).component(0  )				,			2  )
									self.assertEqual((x + y).component(1  )				,			3  )
									self.assertEqual((x + y).component(2  )				,			4  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Vector([1, 2, 3]  )
									_lowercase					       =					Vector([1, 1, 1]  )
									self.assertEqual((x - y).component(0  )				,			0  )
									self.assertEqual((x - y).component(1  )				,			1  )
									self.assertEqual((x - y).component(2  )				,			2  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Vector([1, 2, 3]  )
									_lowercase					       =					Vector([2, -1, 4]  )  # for test of dot product
									_lowercase					       =					Vector([1, -2, -1]  )
									self.assertEqual(str(x * 3.0  )				,			"""(3.0,6.0,9.0)"""  )
									self.assertEqual((a * b)				,			0  )






			def    UpperCamelCase_				(      self  ):
									self.assertEqual(str(zero_vector(10  )  ).count("""0"""  )				,			10  )






			def    UpperCamelCase_				(      self  ):
									self.assertEqual(str(unit_basis_vector(3				,			1  )  )				,			"""(0,1,0)"""  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Vector([1, 2, 3]  )
									_lowercase					       =					Vector([1, 0, 1]  )
									self.assertEqual(str(axpy(2				,			__UpperCamelCase				,			__UpperCamelCase  )  )				,			"""(3,4,7)"""  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Vector([1, 0, 0, 0, 0, 0]  )
									_lowercase					       =					x.copy()
									self.assertEqual(str(__UpperCamelCase  )				,			str(__UpperCamelCase  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Vector([1, 0, 0]  )
									x.change_component(0				,			0  )
									x.change_component(1				,			1  )
									self.assertEqual(str(__UpperCamelCase  )				,			"""(0,1,0)"""  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,			3				,			3  )
									self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n"""				,			str(__UpperCamelCase  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,			3				,			3  )
									_lowercase					       =					[[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
									for x in range(a.height()  ):
															for y in range(a.width()  ):
																					self.assertEqual(minors[x][y]				,			a.minor(__UpperCamelCase				,			__UpperCamelCase  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,			3				,			3  )
									_lowercase					       =					[[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
									for x in range(a.height()  ):
															for y in range(a.width()  ):
																					self.assertEqual(cofactors[x][y]				,			a.cofactor(__UpperCamelCase				,			__UpperCamelCase  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,			3				,			3  )
									self.assertEqual(-5				,			a.determinant()  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]				,			3				,			3  )
									_lowercase					       =					Vector([1, 2, 3]  )
									self.assertEqual("""(14,32,50)"""				,			str(a * x  )  )
									self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n"""				,			str(a * 2  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,			3				,			3  )
									a.change_component(0				,			2				,			5  )
									self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n"""				,			str(__UpperCamelCase  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,			3				,			3  )
									self.assertEqual(7				,			a.component(2				,			1  )				,			0.01  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,			3				,			3  )
									_lowercase					       =					Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]				,			3				,			3  )
									self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n"""				,			str(a + b  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]				,			3				,			3  )
									_lowercase					       =					Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]				,			3				,			3  )
									self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n"""				,			str(a - b  )  )






			def    UpperCamelCase_				(      self  ):
									self.assertEqual(
									    """|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n"""				,			str(square_zero_matrix(5  )  )				,			)


if __name__ == "__main__":
		unittest.main()

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		: Any								=     [
    # 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__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     List[Any]:
						for pegasus_name, hf_name in PATTERNS:
												_lowercase					       =					k.replace(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						return k




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	dict		,   SCREAMING_SNAKE_CASE_	:	dict       )  ->     PegasusForConditionalGeneration:
						_lowercase					       =					DEFAULTS.copy()
						cfg_kwargs.update(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					PegasusConfig(**SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					torch_model.model.state_dict()
						_lowercase					       =					{}
						for k, v in tf_weights.items():
												_lowercase					       =					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:
																		_lowercase					       =					v.T
												_lowercase					       =					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
						_lowercase					       =					torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]       )
						_lowercase					       =					mapping["""shared.weight"""]
						_lowercase					       =					mapping["""shared.weight"""]
						_lowercase					       =					{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_       )
						_lowercase       ,						_lowercase					       =					torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE_		,   strict=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					[
						    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__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]="./ckpt/aeslc/model.ckpt-32000"       )  ->     Dict:
						_lowercase					       =					tf.train.list_variables(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					{}
						_lowercase					       =					["""Adafactor""", """global_step"""]
						for name, shape in tqdm(SCREAMING_SNAKE_CASE_		,   desc="""converting tf checkpoint to dict"""       ):
												_lowercase					       =					any(pat in name for pat in ignore_name       )
												if skip_key:
																		continue
												_lowercase					       =					tf.train.load_variable(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					array
						return tf_weights




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str		,   SCREAMING_SNAKE_CASE_	:	str       )  ->     str:
						# save tokenizer first
						_lowercase					       =					Path(SCREAMING_SNAKE_CASE_       ).parent.name
						_lowercase					       =					task_specific_params[f"""summarization_{dataset}"""]["""max_position_embeddings"""]
						_lowercase					       =					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
						_lowercase					       =					get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					task_specific_params[f"""summarization_{dataset}"""]
						if dataset == "large":
												_lowercase					       =					task_specific_params
						_lowercase					       =					convert_pegasus(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						torch_model.save_pretrained(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					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		: List[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		: str								=     parser.parse_args()
		if args.save_dir is None:
				A		: Tuple								=     Path(args.tf_ckpt_path).parent.name
				A		: List[Any]								=     os.path.join('''pegasus''', dataset)
		convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)