


import unittest

from transformers import BigBirdConfig, 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

							from transformers.models.big_bird.modeling_flax_big_bird import (
							    FlaxBigBirdForCausalLM,
							    FlaxBigBirdForMaskedLM,
							    FlaxBigBirdForMultipleChoice,
							    FlaxBigBirdForPreTraining,
							    FlaxBigBirdForQuestionAnswering,
							    FlaxBigBirdForSequenceClassification,
							    FlaxBigBirdForTokenClassification,
							    FlaxBigBirdModel,
							)

class     A__				(		unittest.TestCase	):




						def __init__(							self       :   Optional[int]					,     _a       :   Dict					,     _a       :   Tuple=2					,     _a       :   List[str]=56					,     _a       :   Optional[Any]=True					,     _a       :   Optional[int]=True					,     _a       :   Any=True					,     _a       :   List[Any]=True					,     _a       :   Dict=99					,     _a       :   Optional[int]=32					,     _a       :   Optional[Any]=2					,     _a       :   Tuple=2					,     _a       :   Optional[int]=7					,     _a       :   Tuple="gelu_new"					,     _a       :   Optional[int]=0.1					,     _a       :   Optional[int]=0.1					,     _a       :   int=512					,     _a       :   Optional[Any]=16					,     _a       :   str=2					,     _a       :   Tuple=0.02					,     _a       :   str=4					,     _a       :   Any="block_sparse"					,     _a       :   int=True					,     _a       :   Optional[int]=False					,     _a       :   List[str]=2					,     _a       :   Union[str, Any]=3					,     )				->  Union[str, Any]:


										"""simple docstring"""





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

										_SCREAMING_SNAKE_CASE      	=rescale_embeddings
										_SCREAMING_SNAKE_CASE      	=attention_type
										_SCREAMING_SNAKE_CASE      	=use_bias
										_SCREAMING_SNAKE_CASE      	=block_size
										_SCREAMING_SNAKE_CASE      	=num_random_blocks




						def 				__UpperCamelCase					(							self       :   Tuple	)				->  Optional[Any]:


										"""simple docstring"""





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

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

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

										_SCREAMING_SNAKE_CASE      	=BigBirdConfig(
										    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=__UpperCamelCase					,     initializer_range=self.initializer_range					,     attention_type=self.attention_type					,     block_size=self.block_size					,     num_random_blocks=self.num_random_blocks					,     use_bias=self.use_bias					,     rescale_embeddings=self.rescale_embeddings					,     )

										return config, input_ids, token_type_ids, attention_mask




						def 				__UpperCamelCase					(							self       :   List[Any]	)				->  Optional[int]:


										"""simple docstring"""





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

@require_flax
class     A__				(		_a ,  unittest.TestCase	):
						UpperCAmelCase   							=    (
						    (
						        FlaxBigBirdForCausalLM,
						        FlaxBigBirdModel,
						        FlaxBigBirdForPreTraining,
						        FlaxBigBirdForMaskedLM,
						        FlaxBigBirdForMultipleChoice,
						        FlaxBigBirdForQuestionAnswering,
						        FlaxBigBirdForSequenceClassification,
						        FlaxBigBirdForTokenClassification,
						    )
						    if is_flax_available()
						    else ()
						)

						UpperCAmelCase   							=    False
						UpperCAmelCase   							=    False




						def 				__UpperCamelCase					(							self       :   Optional[Any]	)				->  str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE      	=FlaxBigBirdModelTester(self	)




						@slow
						# copied from `test_modeling_flax_common` because it takes much longer than other models
						def 				__UpperCamelCase					(							self       :   str	)				->  List[Any]:


										"""simple docstring"""





										super().test_from_pretrained_save_pretrained()




						@slow
						# copied from `test_modeling_flax_common` because it takes much longer than other models
						def 				__UpperCamelCase					(							self       :   List[str]	)				->  Tuple:


										"""simple docstring"""





										super().test_from_pretrained_with_no_automatic_init()




						@slow
						# copied from `test_modeling_flax_common` because it takes much longer than other models
						def 				__UpperCamelCase					(							self       :   Any	)				->  Dict:


										"""simple docstring"""





										super().test_no_automatic_init()




						@slow
						# copied from `test_modeling_flax_common` because it takes much longer than other models
						def 				__UpperCamelCase					(							self       :   Dict	)				->  Optional[int]:


										"""simple docstring"""





										super().test_hidden_states_output()




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


										"""simple docstring"""





										for model_class_name in self.all_model_classes:
														_SCREAMING_SNAKE_CASE      	=model_class_name.from_pretrained('''google/bigbird-roberta-base'''	)
														self.assertIsNotNone(__UpperCamelCase	)




						def 				__UpperCamelCase					(							self       :   Dict	)				->  Optional[Any]:


										"""simple docstring"""





										if self.test_attn_probs:
														super().test_attention_outputs()




						@slow
						# copied from `test_modeling_flax_common` because it takes much longer than other models
						def 				__UpperCamelCase					(							self       :   Union[str, Any]	)				->  int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE      	=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__	):
																		_SCREAMING_SNAKE_CASE      	=self._prepare_for_class(__UpperCamelCase					,     __UpperCamelCase	)
																		_SCREAMING_SNAKE_CASE      	=model_class(__UpperCamelCase	)

																		@jax.jit
																		def model_jitted(_a       :   Optional[int]					,     _a       :   List[Any]=None					,     **_a       :   List[str]	):
																						return model(input_ids=__UpperCamelCase					,     attention_mask=__UpperCamelCase					,     **__UpperCamelCase	)

																		with self.subTest('''JIT Enabled'''	):
																						_SCREAMING_SNAKE_CASE      	=model_jitted(**__UpperCamelCase	).to_tuple()

																		with self.subTest('''JIT Disabled'''	):
																						with jax.disable_jit():
																										_SCREAMING_SNAKE_CASE      	=model_jitted(**__UpperCamelCase	).to_tuple()

																		self.assertEqual(len(__UpperCamelCase	)					,     len(__UpperCamelCase	)	)
																		for jitted_output, output in zip(__UpperCamelCase					,     __UpperCamelCase	):
																						self.assertEqual(jitted_output.shape					,     output.shape	)




						def 				__UpperCamelCase					(							self       :   List[str]					,     _a       :   int					,     _a       :   Union[str, Any]					,     _a       :   Any					,     _a       :   List[Any]=1E-5					,     _a       :   Optional[int]="outputs"					,     _a       :   str=None	)				->  Optional[int]:


										"""simple docstring"""





										if name.startswith('''outputs.attentions'''	):
														return
										else:
														super().check_pt_flax_outputs(__UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase	)

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)