


'''simple docstring'''



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__lowercase     :						Any	=     (
    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     :						List[str]	=     (
    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     :						Dict	=     [
    '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   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Tuple						,				_SCREAMING_SNAKE_CASE				:       Tuple       ):
					for tf_name, hf_name in patterns:
										__a      :	Any    =  k.replace(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
					return k






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Union[str, Any]						,				_SCREAMING_SNAKE_CASE				:       Optional[Any]       ):
					__a      :	Dict    =  BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE       )
					__a      :	Union[str, Any]    =  BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE       )
					__a      :	Any    =  torch_model.state_dict()
					__a      :	Dict    =  {}

					# separating decoder weights
					__a      :	str    =  {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'       )}
					__a      :	Optional[int]    =  {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'       ):
										__a      :	str    =  [k.endswith(_SCREAMING_SNAKE_CASE       ) for ending in KEYS_TO_IGNORE]
										if any(_SCREAMING_SNAKE_CASE       ):
															continue
										__a      :	int    =  DECODER_PATTERNS
										__a      :	Dict    =  rename_state_dict_key(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
										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']       ):
															__a      :	List[Any]    =  v.T
										__a      :	List[Any]    =  torch.from_numpy(_SCREAMING_SNAKE_CASE       )
										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'       ):
										__a      :	str    =  [k.endswith(_SCREAMING_SNAKE_CASE       ) for ending in KEYS_TO_IGNORE]
										if any(_SCREAMING_SNAKE_CASE       ):
															continue
										__a      :	Any    =  REMAINING_PATTERNS
										__a      :	Union[str, Any]    =  rename_state_dict_key(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
										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']       ):
															__a      :	Dict    =  v.T
										__a      :	Optional[Any]    =  torch.from_numpy(_SCREAMING_SNAKE_CASE       )
										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}"""

					__a      :	Union[str, Any]    =  mapping['model.embed_positions.weight']
					__a      :	Optional[Any]    =  mapping.pop('model.embed_positions.weight'       )
					__a   ,    __a      :	Optional[Any]    =  torch_model.load_state_dict(_SCREAMING_SNAKE_CASE						,				strict=_SCREAMING_SNAKE_CASE       )
					__a      :	Optional[Any]    =  [
					    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   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int       ):
					__a      :	Optional[int]    =  tf.train.list_variables(_SCREAMING_SNAKE_CASE       )
					__a      :	str    =  {}
					__a      :	int    =  ['global_step']
					for name, shape in tqdm(_SCREAMING_SNAKE_CASE						,				desc='converting tf checkpoint to dict'       ):
										__a      :	List[Any]    =  any(pat in name for pat in ignore_name       )
										if skip_key:
															continue
										__a      :	Dict    =  tf.train.load_variable(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
										__a      :	List[Any]    =  array
					return tf_weights






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[str]						,				_SCREAMING_SNAKE_CASE				:       List[str]						,				_SCREAMING_SNAKE_CASE				:       Tuple       ):
					__a      :	int    =  get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE       )
					__a      :	Optional[Any]    =  convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
					torch_model.save_pretrained(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
	__lowercase     :						Optional[Any]	=     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     :						List[str]	=     parser.parse_args()
	__lowercase     :						Union[str, Any]	=     {}
	convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







'''simple docstring'''



import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list





@require_torchaudio
@require_sentencepiece
class 							__UpperCamelCase    (		unittest.TestCase ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  'laion/clap-htsat-unfused'
						__a      :	Optional[Any]    =  tempfile.mkdtemp()




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return RobertaTokenizer.from_pretrained(self.checkpoint						,    **__a							)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return ClapFeatureExtractor.from_pretrained(self.checkpoint						,    **__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  self.get_tokenizer()
						__a      :	List[str]    =  self.get_feature_extractor()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						processor.save_pretrained(self.tmpdirname							)
						__a      :	Tuple    =  ClapProcessor.from_pretrained(self.tmpdirname							)

						self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer.get_vocab()							)
						self.assertIsInstance(processor.tokenizer						,    __a							)

						self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  ClapProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						__a      :	int    =  self.get_tokenizer(bos_token='(BOS)'						,    eos_token='(EOS)'							)
						__a      :	List[str]    =  self.get_feature_extractor(do_normalize=__a						,    padding_value=1.0							)

						__a      :	Tuple    =  ClapProcessor.from_pretrained(
						    self.tmpdirname						,    bos_token='(BOS)'						,    eos_token='(EOS)'						,    do_normalize=__a						,    padding_value=1.0							)

						self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer_add_kwargs.get_vocab()							)
						self.assertIsInstance(processor.tokenizer						,    __a							)

						self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor_add_kwargs.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	int    =  self.get_tokenizer()

						__a      :	str    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	int    =  floats_list((3, 1000)							)

						__a      :	str    =  feature_extractor(__a						,    return_tensors='np'							)
						__a      :	int    =  processor(audios=__a						,    return_tensors='np'							)

						for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()						,    input_processor[key].sum()						,    delta=1E-2							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.get_feature_extractor()
						__a      :	Any    =  self.get_tokenizer()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	Union[str, Any]    =  'This is a test string'

						__a      :	Union[str, Any]    =  processor(text=__a							)

						__a      :	Tuple    =  tokenizer(__a							)

						for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]						,    encoded_processor[key]							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	str    =  self.get_tokenizer()

						__a      :	List[str]    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	Dict    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__a      :	Optional[int]    =  processor.batch_decode(__a							)
						__a      :	Optional[Any]    =  tokenizer.batch_decode(__a							)

						self.assertListEqual(__a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.get_feature_extractor()
						__a      :	Optional[int]    =  self.get_tokenizer()

						__a      :	int    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						self.assertListEqual(
						    processor.model_input_names[2:]						,    feature_extractor.model_input_names						,    msg='`processor` and `feature_extractor` model input names do not match'						,    )



