




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__ :   Tuple						=							[
    # 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						(							lowerCamelCase_ :     Any  )       ->  List[Any]:







			"""simple docstring"""





			for pegasus_name, hf_name in PATTERNS:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      k.replace(lowerCamelCase_  ,							lowerCamelCase_  )
			return k





def        __UpperCAmelCase						(							lowerCamelCase_ :     dict  ,							lowerCamelCase_ :     dict  )       ->  PegasusForConditionalGeneration:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Tuple					  =      DEFAULTS.copy()
			cfg_kwargs.update(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      PegasusConfig(**lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      PegasusForConditionalGeneration(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      torch_model.model.state_dict()
			SCREAMING_SNAKE_CASE_   : Dict					  =      {}
			for k, v in tf_weights.items():
						SCREAMING_SNAKE_CASE_   : str					  =      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:
									SCREAMING_SNAKE_CASE_   : str					  =      v.T
						SCREAMING_SNAKE_CASE_   : List[str]					  =      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
			SCREAMING_SNAKE_CASE_   : Dict					  =      torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]  )
			SCREAMING_SNAKE_CASE_   : int					  =      mapping['shared.weight']
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      mapping['shared.weight']
			SCREAMING_SNAKE_CASE_   : Any					  =      {k: torch.zeros_like(lowerCamelCase_  ) for k, v in sd.items() if k.endswith('bias'  ) and k not in mapping}
			mapping.update(**lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[int]					  =      torch_model.model.load_state_dict(lowerCamelCase_  ,							strict=lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      [
			    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						(							lowerCamelCase_ :     List[str]="./ckpt/aeslc/model.ckpt-32000"  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Dict					  =      tf.train.list_variables(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Dict					  =      {}
			SCREAMING_SNAKE_CASE_   : List[str]					  =      ['Adafactor', 'global_step']
			for name, shape in tqdm(lowerCamelCase_  ,							desc='converting tf checkpoint to dict'  ):
						SCREAMING_SNAKE_CASE_   : List[str]					  =      any(pat in name for pat in ignore_name  )
						if skip_key:
									continue
						SCREAMING_SNAKE_CASE_   : str					  =      tf.train.load_variable(lowerCamelCase_  ,							lowerCamelCase_  )
						SCREAMING_SNAKE_CASE_   : List[str]					  =      array
			return tf_weights





def        __UpperCAmelCase						(							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     str  )       ->  Any:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      Path(lowerCamelCase_  ).parent.name
			SCREAMING_SNAKE_CASE_   : int					  =      task_specific_params[F'summarization_{dataset}']['max_position_embeddings']
			SCREAMING_SNAKE_CASE_   : str					  =      PegasusTokenizer.from_pretrained('sshleifer/pegasus'  ,							model_max_length=lowerCamelCase_  )
			assert tok.model_max_length == desired_max_model_length
			tok.save_pretrained(lowerCamelCase_  )

			# convert model
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      get_tf_weights_as_numpy(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : str					  =      task_specific_params[F'summarization_{dataset}']
			if dataset == "large":
						SCREAMING_SNAKE_CASE_   : Tuple					  =      task_specific_params
			SCREAMING_SNAKE_CASE_   : Any					  =      convert_pegasus(lowerCamelCase_  ,							lowerCamelCase_  )
			torch_model.save_pretrained(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      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__":
						UpperCamelCase__ :   List[Any]						=							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__ :   List[Any]						=							parser.parse_args()
						if args.save_dir is None:
												UpperCamelCase__ :   Optional[Any]						=							Path(args.tf_ckpt_path).parent.name
												UpperCamelCase__ :   List[str]						=							os.path.join('''pegasus''', dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)









import os
import sys
import unittest


UpperCamelCase__ :   Optional[Any]						=							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCamelCase__ :   Tuple						=							os.path.join(git_repo_path, '''src''', '''transformers''')

UpperCamelCase__ :   List[Any]						=							'''
{0} = None
'''

UpperCamelCase__ :   str						=							'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


UpperCamelCase__ :   List[Any]						=							'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''







class 				lowerCAmelCase_       (					unittest.TestCase							):






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")' )
							self.assertIsNone(snake_case__ )

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      find_backend('    if not is_tokenizers_available():' )
							self.assertEqual(snake_case__      ,'tokenizers' )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      find_backend('    if not is_tensorflow_text_available():' )
							self.assertEqual(snake_case__      ,'tensorflow_text' )

							SCREAMING_SNAKE_CASE_   : int					  =      find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tokenizers' )

							SCREAMING_SNAKE_CASE_   : str					  =      find_backend(
							    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tensorflow_text' )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      find_backend(
							    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tokenizers_and_vision' )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn('torch'      ,snake_case__ )
							self.assertIn('tensorflow_text'      ,snake_case__ )
							self.assertIn('sentencepiece_and_tokenizers'      ,snake_case__ )

							# Likewise, we can't assert on the exact content of a key
							self.assertIn('BertModel'      ,objects['torch'] )
							self.assertIn('TFBertModel'      ,objects['tf'] )
							self.assertIn('FlaxBertModel'      ,objects['flax'] )
							self.assertIn('BertModel'      ,objects['torch'] )
							self.assertIn('TFBertTokenizer'      ,objects['tensorflow_text'] )
							self.assertIn('convert_slow_tokenizer'      ,objects['sentencepiece_and_tokenizers'] )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      create_dummy_object('CONSTANT'      ,'\'torch\'' )
							self.assertEqual(snake_case__      ,'\nCONSTANT = None\n' )

							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_object('function'      ,'\'torch\'' )
							self.assertEqual(
							    snake_case__      ,'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n' )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_object('FakeClass'      ,'\'torch\'' )
							self.assertEqual(snake_case__      ,snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
							self.assertEqual(dummy_files['torch']      ,snake_case__ )



