





'''simple docstring'''




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


lowercase__       : int         =       [
    # 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     (    __snake_case    :					Optional[int]     )	->	int:
     for pegasus_name, hf_name in PATTERNS:
          __A		:					Optional[Any]						=			k.replace(__snake_case					,				__snake_case     )
     return k






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






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	Tuple:
     # save tokenizer first
     __A		:					int						=			Path(__snake_case     ).parent.name
     __A		:					int						=			task_specific_params[f'summarization_{dataset}']['max_position_embeddings']
     __A		:					str						=			PegasusTokenizer.from_pretrained('sshleifer/pegasus'					,				model_max_length=__snake_case     )
     assert tok.model_max_length == desired_max_model_length
     tok.save_pretrained(__snake_case     )

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


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





'''simple docstring'''



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.roformer.modeling_flax_roformer import (
         FlaxRoFormerForMaskedLM,
         FlaxRoFormerForMultipleChoice,
         FlaxRoFormerForQuestionAnswering,
         FlaxRoFormerForSequenceClassification,
         FlaxRoFormerForTokenClassification,
         FlaxRoFormerModel,
     )





class      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=13     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=99     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=5     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=37     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=512     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=4     ,	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	seq_length
            __snake_case            =	is_training
            __snake_case            =	use_attention_mask
            __snake_case            =	use_token_type_ids
            __snake_case            =	use_labels
            __snake_case            =	vocab_size
            __snake_case            =	hidden_size
            __snake_case            =	num_hidden_layers
            __snake_case            =	num_attention_heads
            __snake_case            =	intermediate_size
            __snake_case            =	hidden_act
            __snake_case            =	hidden_dropout_prob
            __snake_case            =	attention_probs_dropout_prob
            __snake_case            =	max_position_embeddings
            __snake_case            =	type_vocab_size
            __snake_case            =	type_sequence_label_size
            __snake_case            =	initializer_range
            __snake_case            =	num_choices


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size	)

            __snake_case            =	None
            if self.use_attention_mask:
                 __snake_case            =	random_attention_mask([self.batch_size, self.seq_length]	)

            __snake_case            =	None
            if self.use_token_type_ids:
                 __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.type_vocab_size	)

            __snake_case            =	RoFormerConfig(
                vocab_size=self.vocab_size     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	max_position_embeddings=self.max_position_embeddings     ,	type_vocab_size=self.type_vocab_size     ,	is_decoder=__SCREAMING_SNAKE_CASE     ,	initializer_range=self.initializer_range     ,	)

            return config, input_ids, token_type_ids, attention_mask




       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.prepare_config_and_inputs()
            __snake_case       ,     __snake_case       ,     __snake_case       ,     __snake_case            =	config_and_inputs
            __snake_case            =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
            return config, inputs_dict





@require_flax
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Tuple										=      True

       __lowercase	:   Optional[int]										=      (
           (
               FlaxRoFormerModel,
               FlaxRoFormerForMaskedLM,
               FlaxRoFormerForSequenceClassification,
               FlaxRoFormerForTokenClassification,
               FlaxRoFormerForMultipleChoice,
               FlaxRoFormerForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerModelTester(self	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 __snake_case            =	model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''     ,	from_pt=__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model(np.ones((1, 1)	)	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)





@require_flax
class      lowerCAmelCase (			unittest.TestCase):


       @slow
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''	)
            __snake_case            =	jnp.array([[0, 1, 2, 3, 4, 5]]	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)[0]

            __snake_case            =	5_0000

            __snake_case            =	(1, 6, vocab_size)
            self.assertEqual(output.shape     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	jnp.array(
                [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]	)

            self.assertTrue(jnp.allclose(output[:, :3, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-4	)	)






