



"""simple docstring"""


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__A   :							Tuple										=   (
    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
)

__A   :							Optional[int]										=   (
    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
)

__A   :							Union[str, Any]										=   [
    "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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						dict					,     _SCREAMING_SNAKE_CASE   :						dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE				)
				_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(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
								if any(_SCREAMING_SNAKE_CASE				):
												continue
								_UpperCAmelCase					=    DECODER_PATTERNS
								_UpperCAmelCase					=    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''']				):
												_UpperCAmelCase					=    v.T
								_UpperCAmelCase					=    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'''				):
								_UpperCAmelCase					=    [k.endswith(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
								if any(_SCREAMING_SNAKE_CASE				):
												continue
								_UpperCAmelCase					=    REMAINING_PATTERNS
								_UpperCAmelCase					=    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''']				):
												_UpperCAmelCase					=    v.T
								_UpperCAmelCase					=    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}'

				_UpperCAmelCase					=    mapping['''model.embed_positions.weight''']
				_UpperCAmelCase					=    mapping.pop('''model.embed_positions.weight'''				)
				_UpperCAmelCase		,						_UpperCAmelCase					=    torch_model.load_state_dict(_SCREAMING_SNAKE_CASE					,     strict=_SCREAMING_SNAKE_CASE				)
				_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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				_UpperCAmelCase					=    tf.train.list_variables(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    {}
				_UpperCAmelCase					=    ['''global_step''']
				for name, shape in tqdm(_SCREAMING_SNAKE_CASE					,     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(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    array
				return tf_weights







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						dict				):






				'''simple docstring'''



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


if __name__ == "__main__":
					__A   :							Tuple										=   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.")
					__A   :							Dict										=   parser.parse_args()
					__A   :							Tuple										=   {}
					convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)








"""simple docstring"""


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__A   :							List[str]										=   {
    "text_branch": "text_model",
    "audio_branch": "audio_model.audio_encoder",
    "attn": "attention.self",
    "self.proj": "output.dense",
    "attention.self_mask": "attn_mask",
    "mlp.fc1": "intermediate.dense",
    "mlp.fc2": "output.dense",
    "norm1": "layernorm_before",
    "norm2": "layernorm_after",
    "bn0": "batch_norm",
}

__A   :							List[str]										=   AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]=False				):






				'''simple docstring'''



				_UpperCAmelCase		,						_UpperCAmelCase					=    create_model(
				    '''HTSAT-tiny'''					,     '''roberta'''					,     _SCREAMING_SNAKE_CASE					,     precision='''fp32'''					,     device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''					,     enable_fusion=_SCREAMING_SNAKE_CASE					,     fusion_type='''aff_2d''' if enable_fusion else None					,     )
				return model, model_cfg







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    {}

				_UpperCAmelCase					=    r'''.*sequential.(\d+).*'''
				_UpperCAmelCase					=    r'''.*_projection.(\d+).*'''

				for key, value in state_dict.items():
								# check if any key needs to be modified
								for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
												if key_to_modify in key:
																_UpperCAmelCase					=    key.replace(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

								if re.match(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
												# replace sequential layers with list
												_UpperCAmelCase					=    re.match(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				).group(1				)

												_UpperCAmelCase					=    key.replace(f'sequential.{sequential_layer}.'					,     f'layers.{int(_SCREAMING_SNAKE_CASE				)//3}.linear.'				)
								elif re.match(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
												_UpperCAmelCase					=    int(re.match(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				).group(1				)				)

												# Because in CLAP they use `nn.Sequential`...
												_UpperCAmelCase					=    1 if projecton_layer == 0 else 2

												_UpperCAmelCase					=    key.replace(f'_projection.{projecton_layer}.'					,     f'_projection.linear{transformers_projection_layer}.'				)

								if "audio" and "qkv" in key:
												# split qkv into query key and value
												_UpperCAmelCase					=    value
												_UpperCAmelCase					=    mixed_qkv.size(0				) // 3

												_UpperCAmelCase					=    mixed_qkv[:qkv_dim]
												_UpperCAmelCase					=    mixed_qkv[qkv_dim : qkv_dim * 2]
												_UpperCAmelCase					=    mixed_qkv[qkv_dim * 2 :]

												_UpperCAmelCase					=    query_layer
												_UpperCAmelCase					=    key_layer
												_UpperCAmelCase					=    value_layer
								else:
												_UpperCAmelCase					=    value

				return model_state_dict







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						int=False				):






				'''simple docstring'''



				_UpperCAmelCase		,						_UpperCAmelCase					=    init_clap(_SCREAMING_SNAKE_CASE					,     enable_fusion=_SCREAMING_SNAKE_CASE				)

				clap_model.eval()
				_UpperCAmelCase					=    clap_model.state_dict()
				_UpperCAmelCase					=    rename_state_dict(_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    ClapConfig()
				_UpperCAmelCase					=    enable_fusion
				_UpperCAmelCase					=    ClapModel(_SCREAMING_SNAKE_CASE				)

				# ignore the spectrogram embedding layer
				model.load_state_dict(_SCREAMING_SNAKE_CASE					,     strict=_SCREAMING_SNAKE_CASE				)

				model.save_pretrained(_SCREAMING_SNAKE_CASE				)
				transformers_config.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					__A   :							Any										=   argparse.ArgumentParser()
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
					parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
					parser.add_argument("--enable_fusion", action="store_true", help="Whether to enable fusion or not")
					__A   :							int										=   parser.parse_args()

					convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)



