



"""simple docstring"""





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


lowercase  							=					[
    # tf -> hf
    ('''/''', '''.'''),
    ('''layer_''', '''layers.'''),
    ('''kernel''', '''weight'''),
    ('''beta''', '''bias'''),
    ('''gamma''', '''weight'''),
    ('''pegasus''', '''model'''),
]
lowercase  							=					[
    ('''.output.dense''', '''.fc2'''),
    ('''intermediate.LayerNorm''', '''final_layer_norm'''),
    ('''intermediate.dense''', '''fc1'''),
]

lowercase  							=					(
    INIT_COMMON
    + [
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.out_proj'''),
        ('''attention.self''', '''self_attn'''),
        ('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
        ('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
        ('''attention.encdec''', '''encoder_attn'''),
        ('''key''', '''k_proj'''),
        ('''value''', '''v_proj'''),
        ('''query''', '''q_proj'''),
        ('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

lowercase  							=					(
    INIT_COMMON
    + [
        ('''embeddings.word_embeddings''', '''shared.weight'''),
        ('''embeddings.position_embeddings''', '''embed_positions.weight'''),
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.output'''),
        ('''attention.self''', '''self_attn.self'''),
        ('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

lowercase  							=					[
    '''encdec/key/bias''',
    '''encdec/query/bias''',
    '''encdec/value/bias''',
    '''self/key/bias''',
    '''self/query/bias''',
    '''self/value/bias''',
    '''encdec_output/dense/bias''',
    '''attention/output/dense/bias''',
]




def 	UpperCAmelCase     (		A	:						Tuple     ,				A	:						Tuple       ):

		'''simple docstring'''


		for tf_name, hf_name in patterns:
				_UpperCAmelCase							 =		k.replace(_UpperCAmelCase     ,				_UpperCAmelCase       )
		return k




def 	UpperCAmelCase     (		A	:						Union[str, Any]     ,				A	:						Tuple       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		BigBirdPegasusConfig(**_UpperCAmelCase       )
		_UpperCAmelCase							 =		BigBirdPegasusForConditionalGeneration(_UpperCAmelCase       )
		_UpperCAmelCase							 =		torch_model.state_dict()
		_UpperCAmelCase							 =		{}

		# separating decoder weights
		_UpperCAmelCase							 =		{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'       )}
		_UpperCAmelCase							 =		{k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'       )}

		for k, v in tqdm(decoder_weights.items()     ,				'tf -> hf conversion'       ):
				_UpperCAmelCase							 =		[k.endswith(_UpperCAmelCase       ) for ending in KEYS_TO_IGNORE]
				if any(_UpperCAmelCase       ):
						continue
				_UpperCAmelCase							 =		DECODER_PATTERNS
				_UpperCAmelCase							 =		rename_state_dict_key(_UpperCAmelCase     ,				_UpperCAmelCase       )
				if new_k not in state_dict:
						raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})'       )
				if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']       ):
						_UpperCAmelCase							 =		v.T
				_UpperCAmelCase							 =		torch.from_numpy(_UpperCAmelCase       )
				assert v.shape == state_dict[new_k].shape, f'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

		for k, v in tqdm(remaining_weights.items()     ,				'tf -> hf conversion'       ):
				_UpperCAmelCase							 =		[k.endswith(_UpperCAmelCase       ) for ending in KEYS_TO_IGNORE]
				if any(_UpperCAmelCase       ):
						continue
				_UpperCAmelCase							 =		REMAINING_PATTERNS
				_UpperCAmelCase							 =		rename_state_dict_key(_UpperCAmelCase     ,				_UpperCAmelCase       )
				if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
						raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})'       )
				if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']       ):
						_UpperCAmelCase							 =		v.T
				_UpperCAmelCase							 =		torch.from_numpy(_UpperCAmelCase       )
				if k != "pegasus/embeddings/position_embeddings":
						assert v.shape == state_dict[new_k].shape, f'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

		_UpperCAmelCase							 =		mapping['model.embed_positions.weight']
		_UpperCAmelCase							 =		mapping.pop('model.embed_positions.weight'       )
		_UpperCAmelCase ,					_UpperCAmelCase							 =		torch_model.load_state_dict(_UpperCAmelCase     ,				strict=_UpperCAmelCase       )
		_UpperCAmelCase							 =		[
		    k
		    for k in missing
		    if k
		    not in [
		        'final_logits_bias',
		        'model.encoder.embed_tokens.weight',
		        'model.decoder.embed_tokens.weight',
		        'lm_head.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	:						Union[str, Any]       ):

		'''simple docstring'''


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




def 	UpperCAmelCase     (		A	:						List[Any]     ,				A	:						Optional[Any]     ,				A	:						Optional[Any]       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		get_tf_weights_as_numpy(_UpperCAmelCase       )
		_UpperCAmelCase							 =		convert_bigbird_pegasus(_UpperCAmelCase     ,				_UpperCAmelCase       )
		torch_model.save_pretrained(_UpperCAmelCase       )


if __name__ == "__main__":
					lowercase  							=					argparse.ArgumentParser()
					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  							=					parser.parse_args()
					lowercase  							=					{}
					convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







"""simple docstring"""





import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch




@is_pipeline_test
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							_UpperCAmelCase      				=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		TextaTextGenerationPipeline(model=snake_case	,       tokenizer=snake_case						)
									return generator, ["Something to write", "Something else"]






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		generator('Something there'						)
									self.assertEqual(snake_case	,       [{'generated_text': ANY(snake_case						)}]						)
									# These are encoder decoder, they don't just append to incoming string
									self.assertFalse(outputs[0]['generated_text'].startswith('Something there'						)						)

									_UpperCAmelCase							 =		generator(['This is great !', 'Something else']	,       num_return_sequences=2	,       do_sample=snake_case						)
									self.assertEqual(
									    snake_case	,       [
									        [{'generated_text': ANY(snake_case						)}, {'generated_text': ANY(snake_case						)}],
									        [{'generated_text': ANY(snake_case						)}, {'generated_text': ANY(snake_case						)}],
									    ]	,       )

									_UpperCAmelCase							 =		generator(
									    ['This is great !', 'Something else']	,       num_return_sequences=2	,       batch_size=2	,       do_sample=snake_case						)
									self.assertEqual(
									    snake_case	,       [
									        [{'generated_text': ANY(snake_case						)}, {'generated_text': ANY(snake_case						)}],
									        [{'generated_text': ANY(snake_case						)}, {'generated_text': ANY(snake_case						)}],
									    ]	,       )

									with self.assertRaises(snake_case						):
											generator(4						)






							@require_torch
							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		pipeline('text2text-generation'	,       model='patrickvonplaten/t5-tiny-random'	,       framework='pt'						)
									# do_sample=False necessary for reproducibility
									_UpperCAmelCase							 =		generator('Something there'	,       do_sample=snake_case						)
									self.assertEqual(snake_case	,       [{'generated_text': ''}]						)

									_UpperCAmelCase							 =		3
									_UpperCAmelCase							 =		generator(
									    'Something there'	,       num_return_sequences=snake_case	,       num_beams=snake_case	,       )
									_UpperCAmelCase							 =		[
									    {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
									    {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
									    {'generated_text': ''},
									]
									self.assertEqual(snake_case	,       snake_case						)

									_UpperCAmelCase							 =		generator('This is a test'	,       do_sample=snake_case	,       num_return_sequences=2	,       return_tensors=snake_case						)
									self.assertEqual(
									    snake_case	,       [
									        {'generated_token_ids': ANY(torch.Tensor						)},
									        {'generated_token_ids': ANY(torch.Tensor						)},
									    ]	,       )
									_UpperCAmelCase							 =		generator.model.config.eos_token_id
									_UpperCAmelCase							 =		'<pad>'
									_UpperCAmelCase							 =		generator(
									    ['This is a test', 'This is a second test']	,       do_sample=snake_case	,       num_return_sequences=2	,       batch_size=2	,       return_tensors=snake_case	,       )
									self.assertEqual(
									    snake_case	,       [
									        [
									            {'generated_token_ids': ANY(torch.Tensor						)},
									            {'generated_token_ids': ANY(torch.Tensor						)},
									        ],
									        [
									            {'generated_token_ids': ANY(torch.Tensor						)},
									            {'generated_token_ids': ANY(torch.Tensor						)},
									        ],
									    ]	,       )






							@require_tf
							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase							 =		pipeline('text2text-generation'	,       model='patrickvonplaten/t5-tiny-random'	,       framework='tf'						)
									# do_sample=False necessary for reproducibility
									_UpperCAmelCase							 =		generator('Something there'	,       do_sample=snake_case						)
									self.assertEqual(snake_case	,       [{'generated_text': ''}]						)


