




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__lowerCAmelCase	:       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
)

__lowerCAmelCase	:       Tuple     =	(
    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
)

__lowerCAmelCase	:       List[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     __magic_name__       (				A	:      Any,						A	:      Optional[int]     ):






			'''simple docstring'''







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




def     __magic_name__       (				A	:      dict,						A	:      dict     ):






			'''simple docstring'''







			a					=    BigBirdPegasusConfig(**A     )
			a					=    BigBirdPegasusForConditionalGeneration(A     )
			a					=    torch_model.state_dict()
			a					=    {}

			# separating decoder weights
			a					=    {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"     )}
			a					=    {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					=    [k.endswith(A     ) for ending in KEYS_TO_IGNORE]
						if any(A     ):
									continue
						a					=    DECODER_PATTERNS
						a					=    rename_state_dict_key(A,						A     )
						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					=    v.T
						a					=    torch.from_numpy(A     )
						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					=    [k.endswith(A     ) for ending in KEYS_TO_IGNORE]
						if any(A     ):
									continue
						a					=    REMAINING_PATTERNS
						a					=    rename_state_dict_key(A,						A     )
						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					=    v.T
						a					=    torch.from_numpy(A     )
						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					=    mapping["model.embed_positions.weight"]
			a					=    mapping.pop("model.embed_positions.weight"     )
			a					,	a					=    torch_model.load_state_dict(A,						strict=A     )
			a					=    [
			    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     __magic_name__       (				A	:      Dict     ):






			'''simple docstring'''







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




def     __magic_name__       (				A	:      str,						A	:      str,						A	:      dict     ):






			'''simple docstring'''







			a					=    get_tf_weights_as_numpy(A     )
			a					=    convert_bigbird_pegasus(A,						A     )
			torch_model.save_pretrained(A     )


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












import math

import flax.linen as nn
import jax.numpy as jnp




def     __magic_name__       (				A	:      jnp.ndarray,						A	:      int,						A	:      float = 1,						A	:      float = 1,						A	:      float = 1.0E4,						A	:      bool = False,						A	:      float = 1.0,						):






			'''simple docstring'''







			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
			a					=    float(embedding_dim // 2     )
			a					=    math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
			a					=    min_timescale * jnp.exp(jnp.arange(A,						dtype=jnp.floataa     ) * -log_timescale_increment     )
			a					=    jnp.expand_dims(A,						1     ) * jnp.expand_dims(A,						0     )

			# scale embeddings
			a					=    scale * emb

			if flip_sin_to_cos:
						a					=    jnp.concatenate([jnp.cos(A     ), jnp.sin(A     )],						axis=1     )
			else:
						a					=    jnp.concatenate([jnp.sin(A     ), jnp.cos(A     )],						axis=1     )
			a					=    jnp.reshape(A,						[jnp.shape(A     )[0], embedding_dim]     )
			return signal




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							jnp.dtype											=      jnp.floataa





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  Optional[Any]							)      ->  List[Any]:
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_1"							)(__lowerCamelCase							)
							a					=    nn.silu(__lowerCamelCase							)
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_2"							)(__lowerCamelCase							)
							return temb




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							bool											=      False
				SCREAMING_SNAKE_CASE_ :							float											=      1





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  int							)      ->  Union[str, Any]:
							return get_sinusoidal_embeddings(
							    __lowerCamelCase      ,   embedding_dim=self.dim      ,   flip_sin_to_cos=self.flip_sin_to_cos      ,   freq_shift=self.freq_shift							)






