

import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

SCREAMING_SNAKE_CASE_        =      (
    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
)

SCREAMING_SNAKE_CASE_        =      (
    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
)

SCREAMING_SNAKE_CASE_        =      [
    '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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Union[str, Any] )   ->       Tuple:
				for tf_name, hf_name in patterns:
								_UpperCAmelCase       :		List[Any] 		=    k.replace(lowerCAmelCase  ,      lowerCAmelCase )
				return k

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  dict  ,      lowerCAmelCase:  dict )   ->       BigBirdPegasusForConditionalGeneration:
				_UpperCAmelCase       :		Tuple 		=    BigBirdPegasusConfig(**lowerCAmelCase )
				_UpperCAmelCase       :		List[str] 		=    BigBirdPegasusForConditionalGeneration(lowerCAmelCase )
				_UpperCAmelCase       :		List[str] 		=    torch_model.state_dict()
				_UpperCAmelCase       :		Any 		=    {}

				# separating decoder weights
				_UpperCAmelCase       :		Optional[Any] 		=    {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder" )}
				_UpperCAmelCase       :		Optional[Any] 		=    {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       :		Optional[Any] 		=    [k.endswith(lowerCAmelCase ) for ending in KEYS_TO_IGNORE]
								if any(lowerCAmelCase ):
												continue
								_UpperCAmelCase       :		Tuple 		=    DECODER_PATTERNS
								_UpperCAmelCase       :		Any 		=    rename_state_dict_key(lowerCAmelCase  ,      lowerCAmelCase )
								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       :		Any 		=    v.T
								_UpperCAmelCase       :		Any 		=    torch.from_numpy(lowerCAmelCase )
								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       :		str 		=    [k.endswith(lowerCAmelCase ) for ending in KEYS_TO_IGNORE]
								if any(lowerCAmelCase ):
												continue
								_UpperCAmelCase       :		Optional[int] 		=    REMAINING_PATTERNS
								_UpperCAmelCase       :		Optional[Any] 		=    rename_state_dict_key(lowerCAmelCase  ,      lowerCAmelCase )
								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       :		Dict 		=    v.T
								_UpperCAmelCase       :		List[str] 		=    torch.from_numpy(lowerCAmelCase )
								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       :		Union[str, Any] 		=    mapping["model.embed_positions.weight"]
				_UpperCAmelCase       :		List[Any] 		=    mapping.pop("model.embed_positions.weight" )
				_UpperCAmelCase		,							_UpperCAmelCase       :		List[str] 		=    torch_model.load_state_dict(lowerCAmelCase  ,      strict=lowerCAmelCase )
				_UpperCAmelCase       :		Union[str, 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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[Any] )   ->       Dict:
				_UpperCAmelCase       :		int 		=    tf.train.list_variables(lowerCAmelCase )
				_UpperCAmelCase       :		Dict 		=    {}
				_UpperCAmelCase       :		int 		=    ["global_step"]
				for name, shape in tqdm(lowerCAmelCase  ,      desc="converting tf checkpoint to dict" ):
								_UpperCAmelCase       :		List[Any] 		=    any(pat in name for pat in ignore_name )
								if skip_key:
												continue
								_UpperCAmelCase       :		Tuple 		=    tf.train.load_variable(lowerCAmelCase  ,      lowerCAmelCase )
								_UpperCAmelCase       :		Optional[Any] 		=    array
				return tf_weights

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  str  ,      lowerCAmelCase:  dict )   ->       Optional[int]:
				_UpperCAmelCase       :		Any 		=    get_tf_weights_as_numpy(lowerCAmelCase )
				_UpperCAmelCase       :		Optional[Any] 		=    convert_bigbird_pegasus(lowerCAmelCase  ,      lowerCAmelCase )
				torch_model.save_pretrained(lowerCAmelCase )


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





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_        =      'scheduler_config.json'

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			1
						_lowercase    						=			2
						_lowercase    						=			3
						_lowercase    						=			4
						_lowercase    						=			5







@dataclass
class 				a  (	UpperCAmelCase				):
						_lowercase    						=			42







class 				a  :
						_lowercase    						=			SCHEDULER_CONFIG_NAME
						_lowercase    						=			["dtype"]
						_lowercase    						=			[]
						_lowercase    						=			True


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_ = None						,					A_ = None						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    cls.load_config(
										    pretrained_model_name_or_path=A_						,					subfolder=A_						,					return_unused_kwargs=A_						,					**A_						,					)
										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    cls.from_config(A_						,					return_unused_kwargs=A_						,					**A_	)

										if hasattr(A_						,					"create_state"	) and getattr(A_						,					"has_state"						,					A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    scheduler.create_state()

										if return_unused_kwargs:
														return scheduler, state, unused_kwargs

										return scheduler, state


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False						,					**A_	):







										'''simple docstring'''


										self.save_config(save_directory=A_						,					push_to_hub=A_						,					**A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._get_compatibles()





						@classmethod
						def        _UpperCAmelCase				(       cls	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    list(set([cls.__name__] + cls._compatibles	)	)
										_UpperCAmelCase       :		Optional[Any] 		=    importlib.import_module(__name__.split("."	)[0]	)
										_UpperCAmelCase       :		Dict 		=    [
										    getattr(A_						,					A_	) for c in compatible_classes_str if hasattr(A_						,					A_	)
										]
										return compatible_classes







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  Tuple[int] )   ->       jnp.ndarray:
				assert len(lowerCAmelCase ) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase ) - x.ndim) )  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple=0.999  ,      lowerCAmelCase:  int=jnp.floataa )   ->       jnp.ndarray:

				def alpha_bar(lowerCAmelCase:  Union[str, Any] ):
								return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

				_UpperCAmelCase       :		str 		=    []
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Optional[int] 		=    i / num_diffusion_timesteps
								_UpperCAmelCase       :		str 		=    (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar(lowerCAmelCase ) / alpha_bar(lowerCAmelCase )  ,      lowerCAmelCase ) )
				return jnp.array(lowerCAmelCase  ,      dtype=lowerCAmelCase )

@flax.struct.dataclass
class 				a  :
						_lowercase    						=			42
						_lowercase    						=			42
						_lowercase    						=			42


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    scheduler.config

										if config.trained_betas is not None:
														_UpperCAmelCase       :		List[Any] 		=    jnp.asarray(config.trained_betas						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "linear":
														_UpperCAmelCase       :		List[Any] 		=    jnp.linspace(config.beta_start						,					config.beta_end						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														_UpperCAmelCase       :		List[str] 		=    (
														    jnp.linspace(
														        config.beta_start**0.5						,					config.beta_end**0.5						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
														    ** 2
														)
										elif config.beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														_UpperCAmelCase       :		str 		=    betas_for_alpha_bar(config.num_train_timesteps						,					dtype=scheduler.dtype	)
										else:
														raise NotImplementedError(
														    f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'	)

										_UpperCAmelCase       :		Optional[int] 		=    1.0 - betas

										_UpperCAmelCase       :		int 		=    jnp.cumprod(A_						,					axis=0	)

										return cls(
										    alphas=A_						,					betas=A_						,					alphas_cumprod=A_						,					)







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Optional[int] 		=    state.alphas_cumprod

				_UpperCAmelCase       :		Optional[Any] 		=    alphas_cumprod[timesteps] ** 0.5
				_UpperCAmelCase       :		str 		=    sqrt_alpha_prod.flatten()
				_UpperCAmelCase       :		List[Any] 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				_UpperCAmelCase       :		Optional[int] 		=    (1 - alphas_cumprod[timesteps]) ** 0.5
				_UpperCAmelCase       :		List[Any] 		=    sqrt_one_minus_alpha_prod.flatten()
				_UpperCAmelCase       :		int 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       List[Any]:
				_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[Any] 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Dict:
				_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity


